1HYPOTHESIS(1) Hypothesis HYPOTHESIS(1)
2
6 hypothesis - Hypothesis Documentation
7 Hypothesis is a Python library for creating unit tests which are sim‐
9 pler to write and more powerful when run, finding edge cases in your
10 code you wouldn't have thought to look for. It is stable, powerful and
11 easy to add to any existing test suite.
12 It works by letting you write tests that assert that something should
14 be true for every case, not just the ones you happen to think of.
15 Think of a normal unit test as being something like the following:
17 1. Set up some data.
19 2. Perform some operations on the data.
21 3. Assert something about the result.
23 Hypothesis lets you write tests which instead look like this:
25 1. For all data matching some specification.
27 2. Perform some operations on the data.
29 3. Assert something about the result.
31 This is often called property based testing, and was popularised by the
33 Haskell library Quickcheck.
34 It works by generating random data matching your specification and
36 checking that your guarantee still holds in that case. If it finds an
37 example where it doesn't, it takes that example and cuts it down to
38 size, simplifying it until it finds a much smaller example that still
39 causes the problem. It then saves that example for later, so that once
40 it has found a problem with your code it will not forget it in the
41 future.
42 Writing tests of this form usually consists of deciding on guarantees
44 that your code should make - properties that should always hold true,
45 regardless of what the world throws at you. Examples of such guarantees
46 might be:
47 · Your code shouldn't throw an exception, or should only throw a par‐
49 ticular type of exception (this works particularly well if you have a
50 lot of internal assertions).
51 · If you delete an object, it is no longer visible.
53 · If you serialize and then deserialize a value, then you get the same
55 value back.
56 Now you know the basics of what Hypothesis does, the rest of this docu‐
58 mentation will take you through how and why. It's divided into a number
59 of sections, which you can see in the sidebar (or the menu at the top
60 if you're on mobile), but you probably want to begin with the Quick
61 start guide, which will give you a worked example of how to use Hypoth‐
62 esis and a detailed outline of the things you need to know to begin
63 testing your code with it, or check out some of the introductory arti‐
64 cles.
65
67 This document should talk you through everything you need to get
68 started with Hypothesis.
69 An example
71 Suppose we've written a run length encoding system and we want to test
72 it out.
73 We have the following code which I took straight from the Rosetta Code
75 wiki (OK, I removed some commented out code and fixed the formatting,
76 but there are no functional modifications):
77 def encode(input_string):
79 count = 1
80 prev = ''
81 lst = []
82 for character in input_string:
83 if character != prev:
84 if prev:
85 entry = (prev, count)
86 lst.append(entry)
87 count = 1
88 prev = character
89 else:
90 count += 1
91 else:
92 entry = (character, count)
93 lst.append(entry)
94 return lst
95 def decode(lst):
98 q = ''
99 for character, count in lst:
100 q += character * count
101 return q
102 We want to write a test for this that will check some invariant of
104 these functions.
105 The invariant one tends to try when you've got this sort of encoding /
107 decoding is that if you encode something and then decode it then you
108 get the same value back.
109 Lets see how you'd do that with Hypothesis:
111 from hypothesis import given
113 from hypothesis.strategies import text
114 @given(text())
116 def test_decode_inverts_encode(s):
117 assert decode(encode(s)) == s
118 (For this example we'll just let pytest discover and run the test.
120 We'll cover other ways you could have run it later).
121 The text function returns what Hypothesis calls a search strategy. An
123 object with methods that describe how to generate and simplify certain
124 kinds of values. The @given decorator then takes our test function and
125 turns it into a parametrized one which, when called, will run the test
126 function over a wide range of matching data from that strategy.
127 Anyway, this test immediately finds a bug in the code:
129 Falsifying example: test_decode_inverts_encode(s='')
131 UnboundLocalError: local variable 'character' referenced before assignment
133 Hypothesis correctly points out that this code is simply wrong if
135 called on an empty string.
136 If we fix that by just adding the following code to the beginning of
138 the function then Hypothesis tells us the code is correct (by doing
139 nothing as you'd expect a passing test to).
140 if not input_string:
142 return []
143 If we wanted to make sure this example was always checked we could add
145 it in explicitly:
146 from hypothesis import given, example
148 from hypothesis.strategies import text
149 @given(text())
151 @example('')
152 def test_decode_inverts_encode(s):
153 assert decode(encode(s)) == s
154 You don't have to do this, but it can be useful both for clarity pur‐
156 poses and for reliably hitting hard to find examples. Also in local
157 development Hypothesis will just remember and reuse the examples any‐
158 way, but there's not currently a very good workflow for sharing those
159 in your CI.
160 It's also worth noting that both example and given support keyword
162 arguments as well as positional. The following would have worked just
163 as well:
164 @given(s=text())
166 @example(s='')
167 def test_decode_inverts_encode(s):
168 assert decode(encode(s)) == s
169 Suppose we had a more interesting bug and forgot to reset the count
171 each time. Say we missed a line in our encode method:
172 def encode(input_string):
174 count = 1
175 prev = ''
176 lst = []
177 for character in input_string:
178 if character != prev:
179 if prev:
180 entry = (prev, count)
181 lst.append(entry)
182 # count = 1 # Missing reset operation
183 prev = character
184 else:
185 count += 1
186 else:
187 entry = (character, count)
188 lst.append(entry)
189 return lst
190 Hypothesis quickly informs us of the following example:
192 Falsifying example: test_decode_inverts_encode(s='001')
194 Note that the example provided is really quite simple. Hypothesis
196 doesn't just find any counter-example to your tests, it knows how to
197 simplify the examples it finds to produce small easy to understand
198 ones. In this case, two identical values are enough to set the count to
199 a number different from one, followed by another distinct value which
200 should have reset the count but in this case didn't.
201 The examples Hypothesis provides are valid Python code you can run. Any
203 arguments that you explicitly provide when calling the function are not
204 generated by Hypothesis, and if you explicitly provide all the argu‐
205 ments Hypothesis will just call the underlying function once rather
206 than running it multiple times.
207 Installing
209 Hypothesis is available on pypi as "hypothesis". You can install it
210 with:
211 pip install hypothesis
213 If you want to install directly from the source code (e.g. because you
215 want to make changes and install the changed version) you can do this
216 with:
217 pip install -e .
219 You should probably run the tests first to make sure nothing is broken.
221 You can do this with:
222 python setup.py test
224 Note that if they're not already installed this will try to install the
226 test dependencies.
227 You may wish to do all of this in a virtualenv. For example:
229 virtualenv venv
231 source venv/bin/activate
232 pip install hypothesis
233 Will create an isolated environment for you to try hypothesis out in
235 without affecting your system installed packages.
236 Running tests
238 In our example above we just let pytest discover and run our tests, but
239 we could also have run it explicitly ourselves:
240 if __name__ == '__main__':
242 test_decode_inverts_encode()
243 We could also have done this as a python:unittest.TestCase:
245 import unittest
247 class TestEncoding(unittest.TestCase):
249 @given(text())
250 def test_decode_inverts_encode(self, s):
251 self.assertEqual(decode(encode(s)), s)
252 if __name__ == '__main__':
254 unittest.main()
255 A detail: This works because Hypothesis ignores any arguments it hasn't
257 been told to provide (positional arguments start from the right), so
258 the self argument to the test is simply ignored and works as normal.
259 This also means that Hypothesis will play nicely with other ways of
260 parameterizing tests. e.g it works fine if you use pytest fixtures for
261 some arguments and Hypothesis for others.
262 Writing tests
264 A test in Hypothesis consists of two parts: A function that looks like
265 a normal test in your test framework of choice but with some additional
266 arguments, and a @given decorator that specifies how to provide those
267 arguments.
268 Here are some other examples of how you could use that:
270 from hypothesis import given
272 import hypothesis.strategies as st
273 @given(st.integers(), st.integers())
275 def test_ints_are_commutative(x, y):
276 assert x + y == y + x
277 @given(x=st.integers(), y=st.integers())
279 def test_ints_cancel(x, y):
280 assert (x + y) - y == x
281 @given(st.lists(st.integers()))
283 def test_reversing_twice_gives_same_list(xs):
284 # This will generate lists of arbitrary length (usually between 0 and
285 # 100 elements) whose elements are integers.
286 ys = list(xs)
287 ys.reverse()
288 ys.reverse()
289 assert xs == ys
290 @given(st.tuples(st.booleans(), st.text()))
292 def test_look_tuples_work_too(t):
293 # A tuple is generated as the one you provided, with the corresponding
294 # types in those positions.
295 assert len(t) == 2
296 assert isinstance(t[0], bool)
297 assert isinstance(t[1], str)
298 Note that as we saw in the above example you can pass arguments to
300 @given either as positional or as keywords.
301 Where to start
303 You should now know enough of the basics to write some tests for your
304 code using Hypothesis. The best way to learn is by doing, so go have a
305 try.
306 If you're stuck for ideas for how to use this sort of test for your
308 code, here are some good starting points:
309 1. Try just calling functions with appropriate random data and see if
311 they crash. You may be surprised how often this works. e.g. note
312 that the first bug we found in the encoding example didn't even get
313 as far as our assertion: It crashed because it couldn't handle the
314 data we gave it, not because it did the wrong thing.
315 2. Look for duplication in your tests. Are there any cases where you're
317 testing the same thing with multiple different examples? Can you
318 generalise that to a single test using Hypothesis?
319 3. This piece is designed for an F# implementation, but is still very
321 good advice which you may find helps give you good ideas for using
322 Hypothesis.
323 If you have any trouble getting started, don't feel shy about asking
325 for help.
326
328 This is an account of slightly less common Hypothesis features that you
329 don't need to get started but will nevertheless make your life easier.
330 Additional test output
332 Normally the output of a failing test will look something like:
333 Falsifying example: test_a_thing(x=1, y="foo")
335 With the repr of each keyword argument being printed.
337 Sometimes this isn't enough, either because you have values with a repr
339 that isn't very descriptive or because you need to see the output of
340 some intermediate steps of your test. That's where the note function
341 comes in:
342 >>> from hypothesis import given, note, strategies as st
344 >>> @given(st.lists(st.integers()), st.randoms())
345 ... def test_shuffle_is_noop(ls, r):
346 ... ls2 = list(ls)
347 ... r.shuffle(ls2)
348 ... note("Shuffle: %r" % (ls2))
349 ... assert ls == ls2
350 ...
351 >>> try:
352 ... test_shuffle_is_noop()
353 ... except AssertionError:
354 ... print('ls != ls2')
355 Falsifying example: test_shuffle_is_noop(ls=[0, 1], r=RandomWithSeed(18))
356 Shuffle: [1, 0]
357 ls != ls2
358 The note is printed in the final run of the test in order to include
360 any additional information you might need in your test.
361 Test Statistics
363 If you are using pytest you can see a number of statistics about the
364 executed tests by passing the command line argument --hypothe‐
365 sis-show-statistics. This will include some general statistics about
366 the test:
367 For example if you ran the following with --hypothesis-show-statistics:
369 from hypothesis import given, strategies as st
371 @given(st.integers())
373 def test_integers(i):
374 pass
375 You would see:
377 test_integers:
379 - 100 passing examples, 0 failing examples, 0 invalid examples
381 - Typical runtimes: ~ 1ms
382 - Fraction of time spent in data generation: ~ 12%
383 - Stopped because settings.max_examples=100
384 The final "Stopped because" line is particularly important to note: It
386 tells you the setting value that determined when the test should stop
387 trying new examples. This can be useful for understanding the behaviour
388 of your tests. Ideally you'd always want this to be max_examples.
389 In some cases (such as filtered and recursive strategies) you will see
391 events mentioned which describe some aspect of the data generation:
392 from hypothesis import given, strategies as st
394 @given(st.integers().filter(lambda x: x % 2 == 0))
396 def test_even_integers(i):
397 pass
398 You would see something like:
400 test_even_integers:
402 - 100 passing examples, 0 failing examples, 36 invalid examples
404 - Typical runtimes: 0-1 ms
405 - Fraction of time spent in data generation: ~ 16%
406 - Stopped because settings.max_examples=100
407 - Events:
408 * 80.88%, Retried draw from integers().filter(lambda x: <unknown>) to satisfy filter
409 * 26.47%, Aborted test because unable to satisfy integers().filter(lambda x: <unknown>)
410 You can also mark custom events in a test using the event function:
412 hypothesis.event(value)
414 Record an event that occurred this test. Statistics on number of
415 test runs with each event will be reported at the end if you run
416 Hypothesis in statistics reporting mode.
417 Events should be strings or convertible to them.
419 from hypothesis import given, event, strategies as st
421 @given(st.integers().filter(lambda x: x % 2 == 0))
423 def test_even_integers(i):
424 event("i mod 3 = %d" % (i % 3,))
425 You will then see output like:
427 test_even_integers:
429 - 100 passing examples, 0 failing examples, 38 invalid examples
431 - Typical runtimes: 0-1 ms
432 - Fraction of time spent in data generation: ~ 16%
433 - Stopped because settings.max_examples=100
434 - Events:
435 * 80.43%, Retried draw from integers().filter(lambda x: <unknown>) to satisfy filter
436 * 31.88%, i mod 3 = 0
437 * 27.54%, Aborted test because unable to satisfy integers().filter(lambda x: <unknown>)
438 * 21.74%, i mod 3 = 1
439 * 18.84%, i mod 3 = 2
440 Arguments to event can be any hashable type, but two events will be
442 considered the same if they are the same when converted to a string
443 with python:str.
444 Making assumptions
446 Sometimes Hypothesis doesn't give you exactly the right sort of data
447 you want - it's mostly of the right shape, but some examples won't work
448 and you don't want to care about them. You can just ignore these by
449 aborting the test early, but this runs the risk of accidentally testing
450 a lot less than you think you are. Also it would be nice to spend less
451 time on bad examples - if you're running 100 examples per test (the
452 default) and it turns out 70 of those examples don't match your needs,
453 that's a lot of wasted time.
454 hypothesis.assume(condition)
456 Calling assume is like an assert that marks the example as bad,
457 rather than failing the test.
458 This allows you to specify properties that you assume will be
460 true, and let Hypothesis try to avoid similar examples in
461 future.
462 For example suppose you had the following test:
464 @given(floats())
466 def test_negation_is_self_inverse(x):
467 assert x == -(-x)
468 Running this gives us:
470 Falsifying example: test_negation_is_self_inverse(x=float('nan'))
472 AssertionError
473 This is annoying. We know about NaN and don't really care about it, but
475 as soon as Hypothesis finds a NaN example it will get distracted by
476 that and tell us about it. Also the test will fail and we want it to
477 pass.
478 So lets block off this particular example:
480 from math import isnan
482 @given(floats())
484 def test_negation_is_self_inverse_for_non_nan(x):
485 assume(not isnan(x))
486 assert x == -(-x)
487 And this passes without a problem.
489 In order to avoid the easy trap where you assume a lot more than you
491 intended, Hypothesis will fail a test when it can't find enough exam‐
492 ples passing the assumption.
493 If we'd written:
495 @given(floats())
497 def test_negation_is_self_inverse_for_non_nan(x):
498 assume(False)
499 assert x == -(-x)
500 Then on running we'd have got the exception:
502 Unsatisfiable: Unable to satisfy assumptions of hypothesis test_negation_is_self_inverse_for_non_nan. Only 0 examples considered satisfied assumptions
504 How good is assume?
506 Hypothesis has an adaptive exploration strategy to try to avoid things
507 which falsify assumptions, which should generally result in it still
508 being able to find examples in hard to find situations.
509 Suppose we had the following:
511 @given(lists(integers()))
513 def test_sum_is_positive(xs):
514 assert sum(xs) > 0
515 Unsurprisingly this fails and gives the falsifying example [].
517 Adding assume(xs) to this removes the trivial empty example and gives
519 us [0].
520 Adding assume(all(x > 0 for x in xs)) and it passes: the sum of a list
522 of positive integers is positive.
523 The reason that this should be surprising is not that it doesn't find a
525 counter-example, but that it finds enough examples at all.
526 In order to make sure something interesting is happening, suppose we
528 wanted to try this for long lists. e.g. suppose we added an
529 assume(len(xs) > 10) to it. This should basically never find an exam‐
530 ple: a naive strategy would find fewer than one in a thousand examples,
531 because if each element of the list is negative with probability
532 one-half, you'd have to have ten of these go the right way by chance.
533 In the default configuration Hypothesis gives up long before it's tried
534 1000 examples (by default it tries 200).
535 Here's what happens if we try to run this:
537 @given(lists(integers()))
539 def test_sum_is_positive(xs):
540 assume(len(xs) > 10)
541 assume(all(x > 0 for x in xs))
542 print(xs)
543 assert sum(xs) > 0
544 In: test_sum_is_positive()
546 [17, 12, 7, 13, 11, 3, 6, 9, 8, 11, 47, 27, 1, 31, 1]
547 [6, 2, 29, 30, 25, 34, 19, 15, 50, 16, 10, 3, 16]
548 [25, 17, 9, 19, 15, 2, 2, 4, 22, 10, 10, 27, 3, 1, 14, 17, 13, 8, 16, 9, 2...
549 [17, 65, 78, 1, 8, 29, 2, 79, 28, 18, 39]
550 [13, 26, 8, 3, 4, 76, 6, 14, 20, 27, 21, 32, 14, 42, 9, 24, 33, 9, 5, 15, ...
551 [2, 1, 2, 2, 3, 10, 12, 11, 21, 11, 1, 16]
552 As you can see, Hypothesis doesn't find many examples here, but it
554 finds some - enough to keep it happy.
555 In general if you can shape your strategies better to your tests you
557 should - for example integers(1, 1000) is a lot better than assume(1 <=
558 x <= 1000), but assume will take you a long way if you can't.
559 Defining strategies
561 The type of object that is used to explore the examples given to your
562 test function is called a SearchStrategy. These are created using the
563 functions exposed in the hypothesis.strategies module.
564 Many of these strategies expose a variety of arguments you can use to
566 customize generation. For example for integers you can specify min and
567 max values of integers you want. If you want to see exactly what a
568 strategy produces you can ask for an example:
569 >>> integers(min_value=0, max_value=10).example()
571 1
572 Many strategies are built out of other strategies. For example, if you
574 want to define a tuple you need to say what goes in each element:
575 >>> from hypothesis.strategies import tuples
577 >>> tuples(integers(), integers()).example()
578 (-24597, 12566)
579 Further details are available in a separate document.
581 The gory details of given parameters
583 hypothesis.given(*given_arguments, **given_kwargs)
584 A decorator for turning a test function that accepts arguments
585 into a randomized test.
586 This is the main entry point to Hypothesis.
588 The @given decorator may be used to specify which arguments of a func‐
590 tion should be parametrized over. You can use either positional or key‐
591 word arguments or a mixture of the two.
592 For example all of the following are valid uses:
594 @given(integers(), integers())
596 def a(x, y):
597 pass
598 @given(integers())
600 def b(x, y):
601 pass
602 @given(y=integers())
604 def c(x, y):
605 pass
606 @given(x=integers())
608 def d(x, y):
609 pass
610 @given(x=integers(), y=integers())
612 def e(x, **kwargs):
613 pass
614 @given(x=integers(), y=integers())
616 def f(x, *args, **kwargs):
617 pass
618 class SomeTest(TestCase):
621 @given(integers())
622 def test_a_thing(self, x):
623 pass
624 The following are not:
626 @given(integers(), integers(), integers())
628 def g(x, y):
629 pass
630 @given(integers())
632 def h(x, *args):
633 pass
634 @given(integers(), x=integers())
636 def i(x, y):
637 pass
638 @given()
640 def j(x, y):
641 pass
642 The rules for determining what are valid uses of given are as follows:
644 1. You may pass any keyword argument to given.
646 2. Positional arguments to given are equivalent to the rightmost named
648 arguments for the test function.
649 3. Positional arguments may not be used if the underlying test function
651 has varargs, arbitrary keywords, or keyword-only arguments.
652 4. Functions tested with given may not have any defaults.
654 The reason for the "rightmost named arguments" behaviour is so that
656 using @given with instance methods works: self will be passed to the
657 function as normal and not be parametrized over.
658 The function returned by given has all the same arguments as the origi‐
660 nal test, minus those that are filled in by @given.
661 Custom function execution
663 Hypothesis provides you with a hook that lets you control how it runs
664 examples.
665 This lets you do things like set up and tear down around each example,
667 run examples in a subprocess, transform coroutine tests into normal
668 tests, etc. For example, TransactionTestCase in the Django extra runs
669 each example in a separate database transaction.
670 The way this works is by introducing the concept of an executor. An
672 executor is essentially a function that takes a block of code and run
673 it. The default executor is:
674 def default_executor(function):
676 return function()
677 You define executors by defining a method execute_example on a class.
679 Any test methods on that class with @given used on them will use
680 self.execute_example as an executor with which to run tests. For exam‐
681 ple, the following executor runs all its code twice:
682 from unittest import TestCase
684 class TestTryReallyHard(TestCase):
686 @given(integers())
687 def test_something(self, i):
688 perform_some_unreliable_operation(i)
689 def execute_example(self, f):
691 f()
692 return f()
693 Note: The functions you use in map, etc. will run inside the executor.
695 i.e. they will not be called until you invoke the function passed to
696 execute_example.
697 An executor must be able to handle being passed a function which
699 returns None, otherwise it won't be able to run normal test cases. So
700 for example the following executor is invalid:
701 from unittest import TestCase
703 class TestRunTwice(TestCase):
705 def execute_example(self, f):
706 return f()()
707 and should be rewritten as:
709 from unittest import TestCase
711 class TestRunTwice(TestCase):
713 def execute_example(self, f):
714 result = f()
715 if callable(result):
716 result = result()
717 return result
718 An alternative hook is provided for use by test runner extensions such
720 as pytest-trio, which cannot use the execute_example method. This is
721 not recommended for end-users - it is better to write a complete test
722 function directly, perhaps by using a decorator to perform the same
723 transformation before applying @given.
724 @given(x=integers())
726 @pytest.mark.trio
727 async def test(x):
728 ...
729 # Illustrative code, inside the pytest-trio plugin
730 test.hypothesis.inner_test = lambda x: trio.run(test, x)
731 For authors of test runners however, assigning to the inner_test
733 attribute of the hypothesis attribute of the test will replace the
734 interior test.
735 NOTE:
737 The new inner_test must accept and pass through all the *args and
738 **kwargs expected by the original test.
739 If the end user has also specified a custom executor using the exe‐
741 cute_example method, it - and all other execution-time logic - will be
742 applied to the new inner test assigned by the test runner.
743 Using Hypothesis to find values
745 You can use Hypothesis's data exploration features to find values sat‐
746 isfying some predicate. This is generally useful for exploring custom
747 strategies defined with @composite, or experimenting with conditions
748 for filtering data.
749 hypothesis.find(specifier, condition, settings=None, random=None, data‐
751 base_key=None)
752 Returns the minimal example from the given strategy specifier
753 that matches the predicate function condition.
754 >>> from hypothesis import find
756 >>> from hypothesis.strategies import sets, lists, integers
757 >>> find(lists(integers()), lambda x: sum(x) >= 10)
758 [10]
759 >>> find(lists(integers()), lambda x: sum(x) >= 10 and len(x) >= 3)
760 [0, 0, 10]
761 >>> find(sets(integers()), lambda x: sum(x) >= 10 and len(x) >= 3)
762 {0, 1, 9}
763 The first argument to find() describes data in the usual way for an
765 argument to given(), and supports all the same data types. The second
766 is a predicate it must satisfy.
767 Of course not all conditions are satisfiable. If you ask Hypothesis for
769 an example to a condition that is always false it will raise an error:
770 >>> find(integers(), lambda x: False)
772 Traceback (most recent call last):
773 ...
774 hypothesis.errors.NoSuchExample: No examples of condition lambda x: <unknown>
775 (The lambda x: unknown is because Hypothesis can't retrieve the source
777 code of lambdas from the interactive python console. It gives a better
778 error message most of the time which contains the actual condition)
779 Inferred Strategies
781 In some cases, Hypothesis can work out what to do when you omit argu‐
782 ments. This is based on introspection, not magic, and therefore has
783 well-defined limits.
784 builds() will check the signature of the target (using get‐
786 fullargspec()). If there are required arguments with type annotations
787 and no strategy was passed to builds(), from_type() is used to fill
788 them in. You can also pass the special value hypothesis.infer as a
789 keyword argument, to force this inference for arguments with a default
790 value.
791 >>> def func(a: int, b: str):
793 ... return [a, b]
794 >>> builds(func).example()
795 [-6993, '']
796 @given does not perform any implicit inference for required arguments,
798 as this would break compatibility with pytest fixtures. infer can be
799 used as a keyword argument to explicitly fill in an argument from its
800 type annotation.
801 @given(a=infer)
803 def test(a: int): pass
804 # is equivalent to
805 @given(a=integers())
806 def test(a): pass
807 Limitations
809 PEP 3107 type annotations are not supported on Python 2, and Hypothesis
810 does not inspect PEP 484 type comments at runtime. While from_type()
811 will work as usual, inference in builds() and @given will only work if
812 you manually create the __annotations__ attribute (e.g. by using @anno‐
813 tations(...) and @returns(...) decorators). The python:typing module
814 is fully supported on Python 2 if you have the backport installed.
815 The python:typing module is provisional and has a number of internal
817 changes between Python 3.5.0 and 3.6.1, including at minor versions.
818 These are all supported on a best-effort basis, but you may encounter
819 problems with an old version of the module. Please report them to us,
820 and consider updating to a newer version of Python as a workaround.
821 Type Annotations in Hypothesis
823 If you install Hypothesis and use mypy 0.590+, or another PEP 561-com‐
824 patible tool, the type checker should automatically pick up our type
825 hints.
826 NOTE:
828 Hypothesis' type hints may make breaking changes between minor
829 releases.
830 Upstream tools and conventions about type hints remain in flux - for
832 example the python:typing module itself is provisional, and Mypy has
833 not yet reached version 1.0 - and we plan to support the latest ver‐
834 sion of this ecosystem, as well as older versions where practical.
835 We may also find more precise ways to describe the type of various
837 interfaces, or change their type and runtime behaviour togther in a
838 way which is otherwise backwards-compatible. We often omit type
839 hints for deprecated features or arguments, as an additional form of
840 warning.
841 There are known issues inferring the type of examples generated by
843 deferred(), recursive(), one_of(), dictionaries(), and fixed_dictionar‐
844 ies(). We will fix these, and require correspondingly newer versions
845 of Mypy for type hinting, as the ecosystem improves.
846 Writing downstream type hints
848 Projects that provide Hypothesis strategies and use type hints may wish
849 to annotate their strategies too. This is a supported use-case, again
850 on a best-effort provisional basis. For example:
851 def foo_strategy() -> SearchStrategy[Foo]: ...
853 hypothesis.strategies.SearchStrategy is the type of all strategy
855 objects. It is a generic type, and covariant in the type of the exam‐
856 ples it creates. For example:
857 · integers() is of type SearchStrategy[int].
859 · lists(integers()) is of type SearchStrategy[List[int]].
861 · SearchStrategy[Dog] is a subtype of SearchStrategy[Animal] if Dog is
863 a subtype of Animal (as seems likely).
864 WARNING:
866 SearchStrategy should only be used in type hints. Please do not
867 inherit from, compare to, or otherwise use it in any way outside of
868 type hints. The only supported way to construct objects of this
869 type is to use the functions provided by the hypothesis.strategies
870 module!
871 The Hypothesis pytest Plugin
873 Hypothesis includes a tiny plugin to improve integration with pytest,
874 which is activated by default (but does not affect other test runners).
875 It aims to improve the integration between Hypothesis and Pytest by
876 providing extra information and convenient access to config options.
877 · pytest --hypothesis-show-statistics can be used to display test and
879 data generation statistics.
880 · pytest --hypothesis-profile=<profile name> can be used to load a set‐
882 tings profile.
883 · pytest --hypothesis-seed=<an int> can be used to reproduce a failure
885 with a particular seed.
886 Finally, all tests that are defined with Hypothesis automatically have
888 @pytest.mark.hypothesis applied to them. See here for information on
889 working with markers.
890 NOTE:
892 Pytest will load the plugin automatically if Hypothesis is
893 installed. You don't need to do anything at all to use it.
894
896 Hypothesis tries to have good defaults for its behaviour, but sometimes
897 that's not enough and you need to tweak it.
898 The mechanism for doing this is the settings object. You can set up a
900 @given based test to use this using a settings decorator:
901 @given invocation is as follows:
903 from hypothesis import given, settings
905 @given(integers())
907 @settings(max_examples=500)
908 def test_this_thoroughly(x):
909 pass
910 This uses a settings object which causes the test to receive a much
912 larger set of examples than normal.
913 This may be applied either before or after the given and the results
915 are the same. The following is exactly equivalent:
916 from hypothesis import given, settings
918 @settings(max_examples=500)
920 @given(integers())
921 def test_this_thoroughly(x):
922 pass
923 Available settings
925 class hypothesis.settings(parent=None, **kwargs)
926 A settings object controls a variety of parameters that are used
927 in falsification. These may control both the falsification
928 strategy and the details of the data that is generated.
929 Default values are picked up from the settings.default object
931 and changes made there will be picked up in newly created set‐
932 tings.
933 buffer_size
935 The size of the underlying data used to generate exam‐
936 ples. If you need to generate really large examples you
937 may want to increase this, but it will make your tests
938 slower.
939 default value: 8192
941 database
943 An instance of hypothesis.database.ExampleDatabase that
944 will be used to save examples to and load previous exam‐
945 ples from. May be None in which case no storage will be
946 used, :memory: for an in-memory database, or any path for
947 a directory-based example database.
948 default value: (dynamically calculated)
950 database_file
952 The file or directory location to save and load previ‐
953 ously tried examples; :memory: for an in-memory cache or
954 None to disable caching entirely.
955 default value: (dynamically calculated)
957 The database_file setting is deprecated in favor of the
959 database setting, and will be removed in a future ver‐
960 sion. It only exists at all for complicated historical
961 reasons and you should just use database instead.
962 deadline
964 If set, a time in milliseconds (which may be a float to
965 express smaller units of time) that each individual exam‐
966 ple (i.e. each time your test function is called, not the
967 whole decorated test) within a test is not allowed to
968 exceed. Tests which take longer than that may be con‐
969 verted into errors (but will not necessarily be if close
970 to the deadline, to allow some variability in test run
971 time).
972 Set this to None to disable this behaviour entirely.
974 In future this will default to 200. For now, a Hypothe‐
976 sisDeprecationWarning will be emitted if you exceed that
977 default deadline and have not explicitly set a deadline
978 yourself.
979 default value: not_set
981 derandomize
983 If this is True then hypothesis will run in deterministic
984 mode where each falsification uses a random number gener‐
985 ator that is seeded based on the hypothesis to falsify,
986 which will be consistent across multiple runs. This has
987 the advantage that it will eliminate any randomness from
988 your tests, which may be preferable for some situations.
989 It does have the disadvantage of making your tests less
990 likely to find novel breakages.
991 default value: False
993 max_examples
995 Once this many satisfying examples have been considered
996 without finding any counter-example, falsification will
997 terminate.
998 default value: 100
1000 max_iterations
1002 This doesn't actually do anything, but remains for com‐
1003 patibility reasons.
1004 default value: not_set
1006 The max_iterations setting has been disabled, as internal
1008 heuristics are more useful for this purpose than a user
1009 setting. It no longer has any effect.
1010 max_shrinks
1012 Passing max_shrinks=0 disables the shrinking phase (see
1013 the phases setting), but any other value has no effect
1014 and uses a general heuristic.
1015 default value: not_set
1017 The max_shrinks setting has been disabled, as internal
1019 heuristics are more useful for this purpose than a user
1020 setting.
1021 min_satisfying_examples
1023 This doesn't actually do anything, but remains for com‐
1024 patibility reasons.
1025 default value: not_set
1027 The min_satisfying_examples setting has been deprecated
1029 and disabled, due to overlap with the filter_too_much
1030 healthcheck and poor interaction with the max_examples
1031 setting.
1032 perform_health_check
1034 If set to True, Hypothesis will run a preliminary health
1035 check before attempting to actually execute your test.
1036 default value: not_set
1038 This setting is deprecated, as perform_health_check=False
1040 duplicates the effect of sup‐
1041 press_health_check=HealthCheck.all(). Use that instead!
1042 phases Control which phases should be run. See the full documen‐
1044 tation for more details
1045 default value: (<Phase.explicit: 0>, <Phase.reuse: 1>,
1047 <Phase.generate: 2>, <Phase.shrink: 3>)
1048 print_blob
1050 Determines whether to print blobs after tests that can be
1051 used to reproduce failures.
1052 See the documentation on @reproduce_failure for more
1054 details of this behaviour.
1055 default value: <PrintSettings.INFER: 1>
1057 stateful_step_count
1059 Number of steps to run a stateful program for before giv‐
1060 ing up on it breaking.
1061 default value: 50
1063 strict Strict mode has been deprecated in favor of Python's
1065 standard warnings controls. Ironically, enabling it is
1066 therefore an error - it only exists so that users get the
1067 right type of error!
1068 default value: False
1070 Strict mode is deprecated and will go away in a future
1072 version of Hypothesis. To get the same behaviour, use
1073 warnings.simplefilter('error', HypothesisDeprecationWarn‐
1074 ing).
1075 suppress_health_check
1077 A list of health checks to disable.
1078 default value: ()
1080 timeout
1082 Once this many seconds have passed, falsify will termi‐
1083 nate even if it has not found many examples. This is a
1084 soft rather than a hard limit - Hypothesis won't e.g.
1085 interrupt execution of the called function to stop it. If
1086 this value is <= 0 then no timeout will be applied.
1087 default value: 60
1089 The timeout setting is deprecated and will be removed in
1091 a future version of Hypothesis. To get the future behav‐
1092 iour set timeout=hypothesis.unlimited instead (which will
1093 remain valid for a further deprecation period after this
1094 setting has gone away).
1095 use_coverage
1097 Whether to use coverage information to improve Hypothe‐
1098 sis's ability to find bugs.
1099 You should generally leave this turned on unless your
1101 code performs poorly when run under coverage. If you turn
1102 it off, please file a bug report or add a comment to an
1103 existing one about the problem that prompted you to do
1104 so.
1105 default value: True
1107 verbosity
1109 Control the verbosity level of Hypothesis messages
1110 default value: Verbosity.normal
1112 Controlling What Runs
1114 Hypothesis divides tests into four logically distinct phases:
1115 1. Running explicit examples provided with the @example decorator.
1117 2. Rerunning a selection of previously failing examples to reproduce a
1119 previously seen error
1120 3. Generating new examples.
1122 4. Attempting to shrink an example found in phases 2 or 3 to a more
1124 manageable one (explicit examples cannot be shrunk).
1125 The phases setting provides you with fine grained control over which of
1127 these run, with each phase corresponding to a value on the Phase enum:
1128 1. Phase.explicit controls whether explicit examples are run.
1130 2. Phase.reuse controls whether previous examples will be reused.
1132 3. Phase.generate controls whether new examples will be generated.
1134 4. Phase.shrink controls whether examples will be shrunk.
1136 The phases argument accepts a collection with any subset of these. e.g.
1138 settings(phases=[Phase.generate, Phase.shrink]) will generate new exam‐
1139 ples and shrink them, but will not run explicit examples or reuse pre‐
1140 vious failures, while settings(phases=[Phase.explicit]) will only run
1141 the explicit examples.
1142 Seeing intermediate result
1144 To see what's going on while Hypothesis runs your tests, you can turn
1145 up the verbosity setting. This works with both find() and @given.
1146 >>> from hypothesis import find, settings, Verbosity
1148 >>> from hypothesis.strategies import lists, integers
1149 >>> find(lists(integers()), any, settings=settings(verbosity=Verbosity.verbose))
1150 Tried non-satisfying example []
1151 Found satisfying example [-1198601713, -67, 116, -29578]
1152 Shrunk example to [-67, 116, -29578]
1153 Shrunk example to [116, -29578]
1154 Shrunk example to [-29578]
1155 Shrunk example to [-115]
1156 Shrunk example to [115]
1157 Shrunk example to [-57]
1158 Shrunk example to [29]
1159 Shrunk example to [-14]
1160 Shrunk example to [-7]
1161 Shrunk example to [4]
1162 Shrunk example to [2]
1163 Shrunk example to [1]
1164 [1]
1165 The four levels are quiet, normal, verbose and debug. normal is the
1167 default, while in quiet mode Hypothesis will not print anything out,
1168 not even the final falsifying example. debug is basically verbose but a
1169 bit more so. You probably don't want it.
1170 If you are using pytest, you may also need to disable output capturing
1172 for passing tests.
1173 Building settings objects
1175 Settings can be created by calling settings with any of the available
1176 settings values. Any absent ones will be set to defaults:
1177 >>> from hypothesis import settings
1179 >>> settings().max_examples
1180 100
1181 >>> settings(max_examples=10).max_examples
1182 10
1183 You can also pass a 'parent' settings object as the first argument, and
1185 any settings you do not specify as keyword arguments will be copied
1186 from the parent settings:
1187 >>> parent = settings(max_examples=10)
1189 >>> child = settings(parent, deadline=200)
1190 >>> parent.max_examples == child.max_examples == 10
1191 True
1192 >>> parent.deadline
1193 not_set
1194 >>> child.deadline
1195 200
1196 Default settings
1198 At any given point in your program there is a current default settings,
1199 available as settings.default. As well as being a settings object in
1200 its own right, all newly created settings objects which are not explic‐
1201 itly based off another settings are based off the default, so will
1202 inherit any values that are not explicitly set from it.
1203 You can change the defaults by using profiles.
1205 settings Profiles
1207 Depending on your environment you may want different default settings.
1208 For example: during development you may want to lower the number of
1209 examples to speed up the tests. However, in a CI environment you may
1210 want more examples so you are more likely to find bugs.
1211 Hypothesis allows you to define different settings profiles. These pro‐
1213 files can be loaded at any time.
1214 Loading a profile changes the default settings but will not change the
1216 behavior of tests that explicitly change the settings.
1217 >>> from hypothesis import settings
1219 >>> settings.register_profile("ci", max_examples=1000)
1220 >>> settings().max_examples
1221 100
1222 >>> settings.load_profile("ci")
1223 >>> settings().max_examples
1224 1000
1225 Instead of loading the profile and overriding the defaults you can
1227 retrieve profiles for specific tests.
1228 >>> with settings.get_profile("ci"):
1230 ... print(settings().max_examples)
1231 ...
1232 1000
1233 Optionally, you may define the environment variable to load a profile
1235 for you. This is the suggested pattern for running your tests on CI.
1236 The code below should run in a conftest.py or any setup/initialization
1237 section of your test suite. If this variable is not defined the
1238 Hypothesis defined defaults will be loaded.
1239 >>> import os
1241 >>> from hypothesis import settings, Verbosity
1242 >>> settings.register_profile("ci", max_examples=1000)
1243 >>> settings.register_profile("dev", max_examples=10)
1244 >>> settings.register_profile("debug", max_examples=10, verbosity=Verbosity.verbose)
1245 >>> settings.load_profile(os.getenv(u'HYPOTHESIS_PROFILE', 'default'))
1246 If you are using the hypothesis pytest plugin and your profiles are
1248 registered by your conftest you can load one with the command line
1249 option --hypothesis-profile.
1250 $ pytest tests --hypothesis-profile <profile-name>
1252 Timeouts
1254 The timeout functionality of Hypothesis is being deprecated, and will
1255 eventually be removed. For the moment, the timeout setting can still be
1256 set and the old default timeout of one minute remains.
1257 If you want to future proof your code you can get the future behaviour
1259 by setting it to the value hypothesis.unlimited.
1260 from hypothesis import given, settings, unlimited
1262 from hypothesis import strategies as st
1263 @settings(timeout=unlimited)
1265 @given(st.integers())
1266 def test_something_slow(i):
1267 ...
1268 This will cause your code to run until it hits the normal Hypothesis
1270 example limits, regardless of how long it takes. timeout=unlimited will
1271 remain a valid setting after the timeout functionality has been depre‐
1272 cated (but will then have its own deprecation cycle).
1273 There is however now a timing related health check which is designed to
1275 catch tests that run for ages by accident. If you really want your test
1276 to run forever, the following code will enable that:
1277 from hypothesis import given, settings, unlimited, HealthCheck
1279 from hypothesis import strategies as st
1280 @settings(timeout=unlimited, suppress_health_check=[
1282 HealthCheck.hung_test
1283 ])
1284 @given(st.integers())
1285 def test_something_slow(i):
1286 ...
1287
1289 Most things should be easy to generate and everything should be possi‐
1290 ble.
1291 To support this principle Hypothesis provides strategies for most
1293 built-in types with arguments to constrain or adjust the output, as
1294 well as higher-order strategies that can be composed to generate more
1295 complex types.
1296 This document is a guide to what strategies are available for generat‐
1298 ing data and how to build them. Strategies have a variety of other
1299 important internal features, such as how they simplify, but the data
1300 they can generate is the only public part of their API.
1301 Functions for building strategies are all available in the hypothe‐
1303 sis.strategies module. The salient functions from it are as follows:
1304 hypothesis.strategies.nothing()
1306 This strategy never successfully draws a value and will always
1307 reject on an attempt to draw.
1308 Examples from this strategy do not shrink (because there are
1310 none).
1311 hypothesis.strategies.just(value)
1313 Return a strategy which only generates value.
1314 Note: value is not copied. Be wary of using mutable values.
1316 If value is the result of a callable, you can use
1318 builds(callable) instead of just(callable()) to get a fresh
1319 value each time.
1320 Examples from this strategy do not shrink (because there is only
1322 one).
1323 hypothesis.strategies.none()
1325 Return a strategy which only generates None.
1326 Examples from this strategy do not shrink (because there is only
1328 one).
1329 hypothesis.strategies.one_of(*args)
1331 Return a strategy which generates values from any of the argu‐
1332 ment strategies.
1333 This may be called with one iterable argument instead of multi‐
1335 ple strategy arguments. In which case one_of(x) and one_of(*x)
1336 are equivalent.
1337 Examples from this strategy will generally shrink to ones that
1339 come from strategies earlier in the list, then shrink according
1340 to behaviour of the strategy that produced them. In order to get
1341 good shrinking behaviour, try to put simpler strategies first.
1342 e.g. one_of(none(), text()) is better than one_of(text(),
1343 none()).
1344 This is especially important when using recursive strategies.
1346 e.g. x = st.deferred(lambda: st.none() | st.tuples(x, x)) will
1347 shrink well, but x = st.deferred(lambda: st.tuples(x, x) |
1348 st.none()) will shrink very badly indeed.
1349 hypothesis.strategies.integers(min_value=None, max_value=None)
1351 Returns a strategy which generates integers (in Python 2 these
1352 may be ints or longs).
1353 If min_value is not None then all values will be >= min_value.
1355 If max_value is not None then all values will be <= max_value
1356 Examples from this strategy will shrink towards zero, and nega‐
1358 tive values will also shrink towards positive (i.e. -n may be
1359 replaced by +n).
1360 hypothesis.strategies.booleans()
1362 Returns a strategy which generates instances of bool.
1363 Examples from this strategy will shrink towards False (i.e.
1365 shrinking will try to replace True with False where possible).
1366 hypothesis.strategies.floats(min_value=None, max_value=None,
1368 allow_nan=None, allow_infinity=None)
1369 Returns a strategy which generates floats.
1370 · If min_value is not None, all values will be >= min_value.
1372 · If max_value is not None, all values will be <= max_value.
1374 · If min_value or max_value is not None, it is an error to
1376 enable allow_nan.
1377 · If both min_value and max_value are not None, it is an error
1379 to enable allow_infinity.
1380 Where not explicitly ruled out by the bounds, all of infinity,
1382 -infinity and NaN are possible values generated by this strat‐
1383 egy.
1384 Examples from this strategy have a complicated and hard to
1386 explain shrinking behaviour, but it tries to improve "human
1387 readability". Finite numbers will be preferred to infinity and
1388 infinity will be preferred to NaN.
1389 hypothesis.strategies.tuples(*args)
1391 Return a strategy which generates a tuple of the same length as
1392 args by generating the value at index i from args[i].
1393 e.g. tuples(integers(), integers()) would generate a tuple of
1395 length two with both values an integer.
1396 Examples from this strategy shrink by shrinking their component
1398 parts.
1399 hypothesis.strategies.sampled_from(elements)
1401 Returns a strategy which generates any value present in ele‐
1402 ments.
1403 Note that as with just(), values will not be copied and thus you
1405 should be careful of using mutable data.
1406 sampled_from supports ordered collections, as well as Enum
1408 objects. Flag objects may also generate any combination of
1409 their members.
1410 Examples from this strategy shrink by replacing them with values
1412 earlier in the list. So e.g. sampled_from((10, 1)) will shrink
1413 by trying to replace 1 values with 10, and sampled_from((1, 10))
1414 will shrink by trying to replace 10 values with 1.
1415 hypothesis.strategies.lists(elements=None, min_size=None, aver‐
1417 age_size=None, max_size=None, unique_by=None, unique=False)
1418 Returns a list containing values drawn from elements with length
1419 in the interval [min_size, max_size] (no bounds in that direc‐
1420 tion if these are None). If max_size is 0 then elements may be
1421 None and only the empty list will be drawn.
1422 The average_size argument is deprecated. Internal upgrades
1424 since Hypothesis 1.x mean we no longer needed this hint to gen‐
1425 erate useful data.
1426 If unique is True (or something that evaluates to True), we com‐
1428 pare direct object equality, as if unique_by was lambda x: x.
1429 This comparison only works for hashable types.
1430 if unique_by is not None it must be a function returning a hash‐
1432 able type when given a value drawn from elements. The resulting
1433 list will satisfy the condition that for i != j,
1434 unique_by(result[i]) != unique_by(result[j]).
1435 Examples from this strategy shrink by trying to remove elements
1437 from the list, and by shrinking each individual element of the
1438 list.
1439 hypothesis.strategies.sets(elements=None, min_size=None, aver‐
1441 age_size=None, max_size=None)
1442 This has the same behaviour as lists, but returns sets instead.
1443 Note that Hypothesis cannot tell if values are drawn from ele‐
1445 ments are hashable until running the test, so you can define a
1446 strategy for sets of an unhashable type but it will fail at test
1447 time.
1448 Examples from this strategy shrink by trying to remove elements
1450 from the set, and by shrinking each individual element of the
1451 set.
1452 hypothesis.strategies.frozensets(elements=None, min_size=None, aver‐
1454 age_size=None, max_size=None)
1455 This is identical to the sets function but instead returns
1456 frozensets.
1457 hypothesis.strategies.iterables(elements=None, min_size=None, aver‐
1459 age_size=None, max_size=None, unique_by=None, unique=False)
1460 This has the same behaviour as lists, but returns iterables
1461 instead.
1462 Some iterables cannot be indexed (e.g. sets) and some do not
1464 have a fixed length (e.g. generators). This strategy produces
1465 iterators, which cannot be indexed and do not have a fixed
1466 length. This ensures that you do not accidentally depend on
1467 sequence behaviour.
1468 hypothesis.strategies.fixed_dictionaries(mapping)
1470 Generates a dictionary of the same type as mapping with a fixed
1471 set of keys mapping to strategies. mapping must be a dict sub‐
1472 class.
1473 Generated values have all keys present in mapping, with the cor‐
1475 responding values drawn from mapping[key]. If mapping is an
1476 instance of OrderedDict the keys will also be in the same order,
1477 otherwise the order is arbitrary.
1478 Examples from this strategy shrink by shrinking each individual
1480 value in the generated dictionary.
1481 hypothesis.strategies.dictionaries(keys, values, dict_class=<type
1483 'dict'>, min_size=None, average_size=None, max_size=None)
1484 Generates dictionaries of type dict_class with keys drawn from
1485 the keys argument and values drawn from the values argument.
1486 The size parameters have the same interpretation as for lists.
1488 Examples from this strategy shrink by trying to remove keys from
1490 the generated dictionary, and by shrinking each generated key
1491 and value.
1492 hypothesis.strategies.streaming(elements)
1494 Generates an infinite stream of values where each value is drawn
1495 from elements.
1496 The result is iterable (the iterator will never terminate) and
1498 indexable.
1499 Examples from this strategy shrink by trying to shrink each
1501 value drawn.
1502 Deprecated since version 3.15.0: Use data() instead.
1504 hypothesis.strategies.characters(whitelist_categories=None, black‐
1507 list_categories=None, blacklist_characters=None, min_codepoint=None,
1508 max_codepoint=None, whitelist_characters=None)
1509 Generates unicode text type (unicode on python 2, str on python
1510 3) characters following specified filtering rules.
1511 · When no filtering rules are specifed, any character can be
1513 produced.
1514 · If min_codepoint or max_codepoint is specifed, then only char‐
1516 acters having a codepoint in that range will be produced.
1517 · If whitelist_categories is specified, then only characters
1519 from those Unicode categories will be produced. This is a fur‐
1520 ther restriction, characters must also satisfy min_codepoint
1521 and max_codepoint.
1522 · If blacklist_categories is specified, then any character from
1524 those categories will not be produced. Any overlap between
1525 whitelist_categories and blacklist_categories will raise an
1526 exception, as each character can only belong to a single
1527 class.
1528 · If whitelist_characters is specified, then any additional
1530 characters in that list will also be produced.
1531 · If blacklist_characters is specified, then any characters in
1533 that list will be not be produced. Any overlap between
1534 whitelist_characters and blacklist_characters will raise an
1535 exception.
1536 The _codepoint arguments must be integers between zero and
1538 python:sys.max_unicode. The _characters arguments must be col‐
1539 lections of length-one unicode strings, such as a unicode
1540 string.
1541 The _categories arguments must be used to specify either the
1543 one-letter Unicode major category or the two-letter Unicode
1544 general category. For example, ('Nd', 'Lu') signifies "Number,
1545 decimal digit" and "Letter, uppercase". A single letter ('major
1546 category') can be given to match all corresponding categories,
1547 for example 'P' for characters in any punctuation category.
1548 Examples from this strategy shrink towards the codepoint for
1550 '0', or the first allowable codepoint after it if '0' is
1551 excluded.
1552 hypothesis.strategies.text(alphabet=None, min_size=None, aver‐
1554 age_size=None, max_size=None)
1555 Generates values of a unicode text type (unicode on python 2,
1556 str on python 3) with values drawn from alphabet, which should
1557 be an iterable of length one strings or a strategy generating
1558 such. If it is None it will default to generating the full uni‐
1559 code range (excluding surrogate characters). If it is an empty
1560 collection this will only generate empty strings.
1561 min_size and max_size have the usual interpretations.
1563 The average_size argument is deprecated. Internal upgrades
1565 since Hypothesis 1.x mean we no longer needed this hint to gen‐
1566 erate useful data.
1567 Examples from this strategy shrink towards shorter strings, and
1569 with the characters in the text shrinking as per the alphabet
1570 strategy.
1571 hypothesis.strategies.from_regex(regex)
1573 Generates strings that contain a match for the given regex (i.e.
1574 ones for which re.search() will return a non-None result).
1575 regex may be a pattern or compiled regex. Both byte-strings and
1577 unicode strings are supported, and will generate examples of the
1578 same type.
1579 You can use regex flags such as re.IGNORECASE, re.DOTALL or
1581 re.UNICODE to control generation. Flags can be passed either in
1582 compiled regex or inside the pattern with a (?iLmsux) group.
1583 Some regular expressions are only partly supported - the under‐
1585 lying strategy checks local matching and relies on filtering to
1586 resolve context-dependent expressions. Using too many of these
1587 constructs may cause health-check errors as too many examples
1588 are filtered out. This mainly includes (positive or negative)
1589 lookahead and lookbehind groups.
1590 If you want the generated string to match the whole regex you
1592 should use boundary markers. So e.g. r"\A.\Z" will return a sin‐
1593 gle character string, while "." will return any string, and
1594 r"\A.$" will return a single character optionally followed by a
1595 "\n".
1596 Examples from this strategy shrink towards shorter strings and
1598 lower character values.
1599 hypothesis.strategies.binary(min_size=None, average_size=None,
1601 max_size=None)
1602 Generates the appropriate binary type (str in python 2, bytes in
1603 python 3).
1604 min_size and max_size have the usual interpretations.
1606 The average_size argument is deprecated. Internal upgrades
1608 since Hypothesis 1.x mean we no longer needed this hint to gen‐
1609 erate useful data.
1610 Examples from this strategy shrink towards smaller strings and
1612 lower byte values.
1613 hypothesis.strategies.randoms()
1615 Generates instances of Random (actually a Hypothesis specific
1616 RandomWithSeed class which displays what it was initially seeded
1617 with)
1618 Examples from this strategy shrink to seeds closer to zero.
1620 hypothesis.strategies.random_module()
1622 If your code depends on the global random module then you need
1623 to use this.
1624 It will explicitly seed the random module at the start of your
1626 test so that tests are reproducible. The value it passes you is
1627 an opaque object whose only useful feature is that its repr dis‐
1628 plays the random seed. It is not itself a random number genera‐
1629 tor. If you want a random number generator you should use the
1630 randoms() strategy which will give you one.
1631 Examples from these strategy shrink to seeds closer to zero.
1633 hypothesis.strategies.builds(*callable_and_args, **kwargs)
1635 Generates values by drawing from args and kwargs and passing
1636 them to the callable (provided as the first positional argument)
1637 in the appropriate argument position.
1638 e.g. builds(target, integers(), flag=booleans()) would draw an
1640 integer i and a boolean b and call target(i, flag=b).
1641 If the callable has type annotations, they will be used to infer
1643 a strategy for required arguments that were not passed to
1644 builds. You can also tell builds to infer a strategy for an
1645 optional argument by passing the special value hypothesis.infer
1646 as a keyword argument to builds, instead of a strategy for that
1647 argument to the callable.
1648 If the callable is a class defined with attrs, missing required
1650 arguments will be inferred from the attribute on a best-effort
1651 basis, e.g. by checking attrs standard validators.
1652 Examples from this strategy shrink by shrinking the argument
1654 values to the callable.
1655 hypothesis.strategies.from_type(thing)
1657 Looks up the appropriate search strategy for the given type.
1658 from_type is used internally to fill in missing arguments to
1660 builds() and can be used interactively to explore what strate‐
1661 gies are available or to debug type resolution.
1662 You can use register_type_strategy() to handle your custom
1664 types, or to globally redefine certain strategies - for example
1665 excluding NaN from floats, or use timezone-aware instead of
1666 naive time and datetime strategies.
1667 The resolution logic may be changed in a future version, but
1669 currently tries these four options:
1670 1. If thing is in the default lookup mapping or user-registered
1672 lookup, return the corresponding strategy. The default
1673 lookup covers all types with Hypothesis strategies, including
1674 extras where possible.
1675 2. If thing is from the python:typing module, return the corre‐
1677 sponding strategy (special logic).
1678 3. If thing has one or more subtypes in the merged lookup,
1680 return the union of the strategies for those types that are
1681 not subtypes of other elements in the lookup.
1682 4. Finally, if thing has type annotations for all required argu‐
1684 ments, it is resolved via builds().
1685 hypothesis.strategies.fractions(min_value=None, max_value=None,
1687 max_denominator=None)
1688 Returns a strategy which generates Fractions.
1689 If min_value is not None then all generated values are no less
1691 than min_value. If max_value is not None then all generated
1692 values are no greater than max_value. min_value and max_value
1693 may be anything accepted by the Fraction constructor.
1694 If max_denominator is not None then the denominator of any gen‐
1696 erated values is no greater than max_denominator. Note that
1697 max_denominator must be None or a positive integer.
1698 Examples from this strategy shrink towards smaller denominators,
1700 then closer to zero.
1701 hypothesis.strategies.decimals(min_value=None, max_value=None,
1703 allow_nan=None, allow_infinity=None, places=None)
1704 Generates instances of decimals.Decimal, which may be:
1705 · A finite rational number, between min_value and max_value.
1707 · Not a Number, if allow_nan is True. None means "allow NaN,
1709 unless min_value and max_value are not None".
1710 · Positive or negative infinity, if max_value and min_value
1712 respectively are None, and allow_infinity is not False. None
1713 means "allow infinity, unless excluded by the min and max val‐
1714 ues".
1715 Note that where floats have one NaN value, Decimals have four:
1717 signed, and either quiet or signalling. See the decimal module
1718 docs for more information on special values.
1719 If places is not None, all finite values drawn from the strategy
1721 will have that number of digits after the decimal place.
1722 Examples from this strategy do not have a well defined shrink
1724 order but try to maximize human readability when shrinking.
1725 hypothesis.strategies.recursive(base, extend, max_leaves=100)
1727 base: A strategy to start from.
1728 extend: A function which takes a strategy and returns a new
1730 strategy.
1731 max_leaves: The maximum number of elements to be drawn from base
1733 on a given run.
1734 This returns a strategy S such that S = extend(base | S). That
1736 is, values may be drawn from base, or from any strategy reach‐
1737 able by mixing applications of | and extend.
1738 An example may clarify: recursive(booleans(), lists) would
1740 return a strategy that may return arbitrarily nested and mixed
1741 lists of booleans. So e.g. False, [True], [False, []], and
1742 [[[[True]]]] are all valid values to be drawn from that strat‐
1743 egy.
1744 Examples from this strategy shrink by trying to reduce the
1746 amount of recursion and by shrinking according to the shrinking
1747 behaviour of base and the result of extend.
1748 hypothesis.strategies.permutations(values)
1750 Return a strategy which returns permutations of the ordered col‐
1751 lection values.
1752 Examples from this strategy shrink by trying to become closer to
1754 the original order of values.
1755 hypothesis.strategies.datetimes(min_value=datetime.datetime(1, 1, 1, 0,
1757 0), max_value=datetime.datetime(9999, 12, 31, 23, 59, 59, 999999),
1758 timezones=none(), min_datetime=None, max_datetime=None)
1759 A strategy for generating datetimes, which may be time‐
1760 zone-aware.
1761 This strategy works by drawing a naive datetime between
1763 min_datetime and max_datetime, which must both be naive (have no
1764 timezone).
1765 timezones must be a strategy that generates tzinfo objects (or
1767 None, which is valid for naive datetimes). A value drawn from
1768 this strategy will be added to a naive datetime, and the result‐
1769 ing tz-aware datetime returned.
1770 NOTE:
1772 tz-aware datetimes from this strategy may be ambiguous or
1773 non-existent due to daylight savings, leap seconds, timezone
1774 and calendar adjustments, etc. This is intentional, as mal‐
1775 formed timestamps are a common source of bugs.
1776 hypothesis.extra.pytz.timezones() requires the pytz package, but
1778 provides all timezones in the Olsen database. If you want to
1779 allow naive datetimes, combine strategies like none() | time‐
1780 zones().
1781 hypothesis.extra.dateutil.timezones() requires the
1783 python-dateutil package, and similarly provides all timezones
1784 there.
1785 Alternatively, you can create a list of the timezones you wish
1787 to allow (e.g. from the standard library, datetutil, or pytz)
1788 and use sampled_from(). Ensure that simple values such as None
1789 or UTC are at the beginning of the list for proper minimisation.
1790 Examples from this strategy shrink towards midnight on January
1792 1st 2000.
1793 The following arguments have been renamed:
1795 · min_datetime has been renamed to min_value
1797 · max_datetime has been renamed to max_value
1799 Use of the old names has been deprecated and will be removed in
1801 a future version of Hypothesis.
1802 hypothesis.strategies.dates(min_value=datetime.date(1, 1, 1),
1804 max_value=datetime.date(9999, 12, 31), min_date=None, max_date=None)
1805 A strategy for dates between min_date and max_date.
1806 Examples from this strategy shrink towards January 1st 2000.
1808 The following arguments have been renamed:
1810 · max_date has been renamed to max_value
1812 · min_date has been renamed to min_value
1814 Use of the old names has been deprecated and will be removed in
1816 a future version of Hypothesis.
1817 hypothesis.strategies.times(min_value=datetime.time(0, 0),
1819 max_value=datetime.time(23, 59, 59, 999999), timezones=none(),
1820 min_time=None, max_time=None)
1821 A strategy for times between min_time and max_time.
1822 The timezones argument is handled as for datetimes().
1824 Examples from this strategy shrink towards midnight, with the
1826 timezone component shrinking as for the strategy that provided
1827 it.
1828 The following arguments have been renamed:
1830 · min_time has been renamed to min_value
1832 · max_time has been renamed to max_value
1834 Use of the old names has been deprecated and will be removed in
1836 a future version of Hypothesis.
1837 hypothesis.strategies.timedeltas(min_value=date‐
1839 time.timedelta(-999999999), max_value=datetime.timedelta(999999999,
1840 86399, 999999), min_delta=None, max_delta=None)
1841 A strategy for timedeltas between min_value and max_value.
1842 Examples from this strategy shrink towards zero.
1844 The following arguments have been renamed:
1846 · max_delta has been renamed to max_value
1848 · min_delta has been renamed to min_value
1850 Use of the old names has been deprecated and will be removed in
1852 a future version of Hypothesis.
1853 hypothesis.strategies.composite(f)
1855 Defines a strategy that is built out of potentially arbitrarily
1856 many other strategies.
1857 This is intended to be used as a decorator. See the full docu‐
1859 mentation for more details about how to use this function.
1860 Examples from this strategy shrink by shrinking the output of
1862 each draw call.
1863 hypothesis.strategies.complex_numbers(min_magnitude=0, max_magni‐
1865 tude=None, allow_infinity=None, allow_nan=None)
1866 Returns a strategy that generates complex numbers.
1867 This strategy draws complex numbers with constrained magnitudes.
1869 The min_magnitude and max_magnitude parameters should be
1870 non-negative Real numbers; values of None correspond to zero and
1871 infinite values respectively.
1872 If min_magnitude is positive or max_magnitude is finite, it is
1874 an error to enable allow_nan. If max_magnitude is finite, it is
1875 an error to enable allow_infinity.
1876 The magnitude contraints are respected up to a relative error of
1878 (around) floating-point epsilon, due to implementation via the
1879 system sqrt function.
1880 Examples from this strategy shrink by shrinking their real and
1882 imaginary parts, as floats().
1883 If you need to generate complex numbers with particular real and
1885 imaginary parts or relationships between parts, consider using
1886 builds(complex, ...) <hypothesis.strategies.builds> or @compos‐
1887 ite <hypothesis.strategies.composite> respectively.
1888 hypothesis.strategies.shared(base, key=None)
1890 Returns a strategy that draws a single shared value per run,
1891 drawn from base. Any two shared instances with the same key will
1892 share the same value, otherwise the identity of this strategy
1893 will be used. That is:
1894 >>> s = integers() # or any other strategy
1896 >>> x = shared(s)
1897 >>> y = shared(s)
1898 In the above x and y may draw different (or potentially the
1900 same) values. In the following they will always draw the same:
1901 >>> x = shared(s, key="hi")
1903 >>> y = shared(s, key="hi")
1904 Examples from this strategy shrink as per their base strategy.
1906 hypothesis.strategies.choices()
1908 Strategy that generates a function that behaves like ran‐
1909 dom.choice.
1910 Will note choices made for reproducibility.
1912 Deprecated since version 3.15.0: Use data() with sampled_from()
1914 instead.
1915 Examples from this strategy shrink by making each choice func‐
1918 tion return an earlier value in the sequence passed to it.
1919 hypothesis.strategies.uuids(version=None)
1921 Returns a strategy that generates UUIDs.
1922 If the optional version argument is given, value is passed
1924 through to UUID and only UUIDs of that version will be gener‐
1925 ated.
1926 All returned values from this will be unique, so e.g. if you do
1928 lists(uuids()) the resulting list will never contain duplicates.
1929 Examples from this strategy don't have any meaningful shrink
1931 order.
1932 hypothesis.strategies.runner(default=not_set)
1934 A strategy for getting "the current test runner", whatever that
1935 may be. The exact meaning depends on the entry point, but it
1936 will usually be the associated 'self' value for it.
1937 If there is no current test runner and a default is provided,
1939 return that default. If no default is provided, raises Invali‐
1940 dArgument.
1941 Examples from this strategy do not shrink (because there is only
1943 one).
1944 hypothesis.strategies.data()
1946 This isn't really a normal strategy, but instead gives you an
1947 object which can be used to draw data interactively from other
1948 strategies.
1949 It can only be used within @given, not find(). This is because
1951 the lifetime of the object cannot outlast the test body.
1952 See the rest of the documentation for more complete information.
1954 Examples from this strategy do not shrink (because there is only
1956 one), but the result of calls to each draw() call shrink as they
1957 normally would.
1958 hypothesis.strategies.register_type_strategy(custom_type, strategy)
1960 Add an entry to the global type-to-strategy lookup.
1961 This lookup is used in builds() and @given.
1963 builds() will be used automatically for classes with type anno‐
1965 tations on __init__ , so you only need to register a strategy if
1966 one or more arguments need to be more tightly defined than their
1967 type-based default, or if you want to supply a strategy for an
1968 argument with a default value.
1969 strategy may be a search strategy, or a function that takes a
1971 type and returns a strategy (useful for generic types).
1972 hypothesis.strategies.deferred(definition)
1974 A deferred strategy allows you to write a strategy that refer‐
1975 ences other strategies that have not yet been defined. This
1976 allows for the easy definition of recursive and mutually recur‐
1977 sive strategies.
1978 The definition argument should be a zero-argument function that
1980 returns a strategy. It will be evaluated the first time the
1981 strategy is used to produce an example.
1982 Example usage:
1984 >>> import hypothesis.strategies as st
1986 >>> x = st.deferred(lambda: st.booleans() | st.tuples(x, x))
1987 >>> x.example()
1988 (((False, (True, True)), (False, True)), (True, True))
1989 >>> x.example()
1990 True
1991 Mutual recursion also works fine:
1993 >>> a = st.deferred(lambda: st.booleans() | b)
1995 >>> b = st.deferred(lambda: st.tuples(a, a))
1996 >>> a.example()
1997 True
1998 >>> b.example()
1999 (False, (False, ((False, True), False)))
2000 Examples from this strategy shrink as they normally would from
2002 the strategy returned by the definition.
2003 hypothesis.strategies.emails()
2005 A strategy for generating email addresses as unicode strings.
2006 The address format is specific in RFC 5322#section-3.4.1. Values
2007 shrink towards shorter local-parts and host domains.
2008 This strategy is useful for generating "user data" for tests, as
2010 mishandling of email addresses is a common source of bugs.
2011 Future updates will generate more complicated addresses allowed
2012 by the RFC.
2013 Shrinking
2015 When using strategies it is worth thinking about how the data shrinks.
2016 Shrinking is the process by which Hypothesis tries to produce human
2017 readable examples when it finds a failure - it takes a complex example
2018 and turns it into a simpler one.
2019 Each strategy defines an order in which it shrinks - you won't usually
2021 need to care about this much, but it can be worth being aware of as it
2022 can affect what the best way to write your own strategies is.
2023 The exact shrinking behaviour is not a guaranteed part of the API, but
2025 it doesn't change that often and when it does it's usually because we
2026 think the new way produces nicer examples.
2027 Possibly the most important one to be aware of is one_of(), which has a
2029 preference for values produced by strategies earlier in its argument
2030 list. Most of the others should largely "do the right thing" without
2031 you having to think about it.
2032 Adapting strategies
2034 Often it is the case that a strategy doesn't produce exactly what you
2035 want it to and you need to adapt it. Sometimes you can do this in the
2036 test, but this hurts reuse because you then have to repeat the adaption
2037 in every test.
2038 Hypothesis gives you ways to build strategies from other strategies
2040 given functions for transforming the data.
2041 Mapping
2043 map is probably the easiest and most useful of these to use. If you
2044 have a strategy s and a function f, then an example s.map(f).example()
2045 is f(s.example()), i.e. we draw an example from s and then apply f to
2046 it.
2047 e.g.:
2049 >>> lists(integers()).map(sorted).example()
2051 [-25527, -24245, -23118, -93, -70, -7, 0, 39, 40, 65, 88, 112, 6189, 9480, 19469, 27256, 32526, 1566924430]
2052 Note that many things that you might use mapping for can also be done
2054 with builds().
2055 Filtering
2057 filter lets you reject some examples. s.filter(f).example() is some
2058 example of s such that f(example) is truthy.
2059 >>> integers().filter(lambda x: x > 11).example()
2061 26126
2062 >>> integers().filter(lambda x: x > 11).example()
2063 23324
2064 It's important to note that filter isn't magic and if your condition is
2066 too hard to satisfy then this can fail:
2067 >>> integers().filter(lambda x: False).example()
2069 Traceback (most recent call last):
2070 ...
2071 hypothesis.errors.NoExamples: Could not find any valid examples in 20 tries
2072 In general you should try to use filter only to avoid corner cases that
2074 you don't want rather than attempting to cut out a large chunk of the
2075 search space.
2076 A technique that often works well here is to use map to first transform
2078 the data and then use filter to remove things that didn't work out. So
2079 for example if you wanted pairs of integers (x,y) such that x < y you
2080 could do the following:
2081 >>> tuples(integers(), integers()).map(sorted).filter(lambda x: x[0] < x[1]).example()
2083 [-8543729478746591815, 3760495307320535691]
2084 Chaining strategies together
2086 Finally there is flatmap. flatmap draws an example, then turns that
2087 example into a strategy, then draws an example from that strategy.
2088 It may not be obvious why you want this at first, but it turns out to
2090 be quite useful because it lets you generate different types of data
2091 with relationships to each other.
2092 For example suppose we wanted to generate a list of lists of the same
2094 length:
2095 >>> rectangle_lists = integers(min_value=0, max_value=10).flatmap(
2097 ... lambda n: lists(lists(integers(), min_size=n, max_size=n)))
2098 >>> find(rectangle_lists, lambda x: True)
2099 []
2100 >>> find(rectangle_lists, lambda x: len(x) >= 10)
2101 [[], [], [], [], [], [], [], [], [], []]
2102 >>> find(rectangle_lists, lambda t: len(t) >= 3 and len(t[0]) >= 3)
2103 [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
2104 >>> find(rectangle_lists, lambda t: sum(len(s) for s in t) >= 10)
2105 [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]
2106 In this example we first choose a length for our tuples, then we build
2108 a strategy which generates lists containing lists precisely of that
2109 length. The finds show what simple examples for this look like.
2110 Most of the time you probably don't want flatmap, but unlike filter and
2112 map which are just conveniences for things you could just do in your
2113 tests, flatmap allows genuinely new data generation that you wouldn't
2114 otherwise be able to easily do.
2115 (If you know Haskell: Yes, this is more or less a monadic bind. If you
2117 don't know Haskell, ignore everything in these parentheses. You do not
2118 need to understand anything about monads to use this, or anything else
2119 in Hypothesis).
2120 Recursive data
2122 Sometimes the data you want to generate has a recursive definition.
2123 e.g. if you wanted to generate JSON data, valid JSON is:
2124 1. Any float, any boolean, any unicode string.
2126 2. Any list of valid JSON data
2128 3. Any dictionary mapping unicode strings to valid JSON data.
2130 The problem is that you cannot call a strategy recursively and expect
2132 it to not just blow up and eat all your memory. The other problem here
2133 is that not all unicode strings display consistently on different
2134 machines, so we'll restrict them in our doctest.
2135 The way Hypothesis handles this is with the recursive() function which
2137 you pass in a base case and a function that, given a strategy for your
2138 data type, returns a new strategy for it. So for example:
2139 >>> from string import printable; from pprint import pprint
2141 >>> json = recursive(none() | booleans() | floats() | text(printable),
2142 ... lambda children: lists(children) | dictionaries(text(printable), children))
2143 >>> pprint(json.example())
2144 ['dy',
2145 [None, True, 6.297399055778002e+16, False],
2146 {'a{h\\:694K~{mY>a1yA:#CmDYb': None},
2147 '\\kP!4',
2148 {'#1J1': '',
2149 'cx.': None,
2150 "jv'A?qyp_sB\n$62g": [],
2151 'qgnP': [False, -inf, 'la)']},
2152 [],
2153 {}]
2154 >>> pprint(json.example())
2155 {'': None,
2156 '(Rt)': 1.192092896e-07,
2157 ',': [],
2158 '6': 2.2250738585072014e-308,
2159 'HA=/': [],
2160 'YU]gy8': inf,
2161 'l': None,
2162 'nK': False}
2163 >>> pprint(json.example())
2164 []
2165 That is, we start with our leaf data and then we augment it by allowing
2167 lists and dictionaries of anything we can generate as JSON data.
2168 The size control of this works by limiting the maximum number of values
2170 that can be drawn from the base strategy. So for example if we wanted
2171 to only generate really small JSON we could do this as:
2172 >>> small_lists = recursive(booleans(), lists, max_leaves=5)
2174 >>> small_lists.example()
2175 [False]
2176 >>> small_lists.example()
2177 True
2178 >>> small_lists.example()
2179 []
2180 Composite strategies
2182 The @composite decorator lets you combine other strategies in more or
2183 less arbitrary ways. It's probably the main thing you'll want to use
2184 for complicated custom strategies.
2185 The composite decorator works by converting a function that returns one
2187 example into a function that returns a strategy that produces such
2188 examples - which you can pass to @given, modify with .map or .filter,
2189 and generally use like any other strategy.
2190 It does this by giving you a special function draw as the first argu‐
2192 ment, which can be used just like the corresponding method of the
2193 data() strategy within a test. In fact, the implementation is almost
2194 the same - but defining a strategy with @composite makes code reuse
2195 easier, and usually improves the display of failing examples.
2196 For example, the following gives you a list and an index into it:
2198 >>> @composite
2200 ... def list_and_index(draw, elements=integers()):
2201 ... xs = draw(lists(elements, min_size=1))
2202 ... i = draw(integers(min_value=0, max_value=len(xs) - 1))
2203 ... return (xs, i)
2204 draw(s) is a function that should be thought of as returning s.exam‐
2206 ple(), except that the result is reproducible and will minimize cor‐
2207 rectly. The decorated function has the initial argument removed from
2208 the list, but will accept all the others in the expected order.
2209 Defaults are preserved.
2210 >>> list_and_index()
2212 list_and_index()
2213 >>> list_and_index().example()
2214 ([-21904], 0)
2215 >>> list_and_index(booleans())
2217 list_and_index(elements=booleans())
2218 >>> list_and_index(booleans()).example()
2219 ([True], 0)
2220 Note that the repr will work exactly like it does for all the built-in
2222 strategies: it will be a function that you can call to get the strategy
2223 in question, with values provided only if they do not match the
2224 defaults.
2225 You can use assume inside composite functions:
2227 @composite
2229 def distinct_strings_with_common_characters(draw):
2230 x = draw(text(), min_size=1)
2231 y = draw(text(alphabet=x))
2232 assume(x != y)
2233 return (x, y)
2234 This works as assume normally would, filtering out any examples for
2236 which the passed in argument is falsey.
2237 Drawing interactively in tests
2239 There is also the data() strategy, which gives you a means of using
2240 strategies interactively. Rather than having to specify everything up
2241 front in @given you can draw from strategies in the body of your test:
2242 @given(data())
2244 def test_draw_sequentially(data):
2245 x = data.draw(integers())
2246 y = data.draw(integers(min_value=x))
2247 assert x < y
2248 If the test fails, each draw will be printed with the falsifying exam‐
2250 ple. e.g. the above is wrong (it has a boundary condition error), so
2251 will print:
2252 Falsifying example: test_draw_sequentially(data=data(...))
2254 Draw 1: 0
2255 Draw 2: 0
2256 As you can see, data drawn this way is simplified as usual.
2258 Test functions using the data() strategy do not support explicit @exam‐
2260 ple(...)s. In this case, the best option is usually to construct your
2261 data with @composite or the explicit example, and unpack this within
2262 the body of the test.
2263 Optionally, you can provide a label to identify values generated by
2265 each call to data.draw(). These labels can be used to identify values
2266 in the output of a falsifying example.
2267 For instance:
2269 @given(data())
2271 def test_draw_sequentially(data):
2272 x = data.draw(integers(), label='First number')
2273 y = data.draw(integers(min_value=x), label='Second number')
2274 assert x < y
2275 will produce the output:
2277 Falsifying example: test_draw_sequentially(data=data(...))
2279 Draw 1 (First number): 0
2280 Draw 2 (Second number): 0
2281
2283 Hypothesis itself does not have any dependencies, but there are some
2284 packages that need additional things installed in order to work.
2285 You can install these dependencies using the setuptools extra feature
2287 as e.g. pip install hypothesis[django]. This will check installation
2288 of compatible versions.
2289 You can also just install hypothesis into a project using them, ignore
2291 the version constraints, and hope for the best.
2292 In general "Which version is Hypothesis compatible with?" is a hard
2294 question to answer and even harder to regularly test. Hypothesis is
2295 always tested against the latest compatible version and each package
2296 will note the expected compatibility range. If you run into a bug with
2297 any of these please specify the dependency version.
2298 There are separate pages for django and numpy.
2300 hypothesis[pytz]
2302 This module provides pytz timezones.
2303 You can use this strategy to make hypothesis.strategies.datetimes() and
2305 hypothesis.strategies.times() produce timezone-aware values.
2306 hypothesis.extra.pytz.timezones()
2308 Any timezone in the Olsen database, as a pytz tzinfo object.
2309 This strategy minimises to UTC, or the smallest possible fixed
2311 offset, and is designed for use with hypothesis.strategies.date‐
2312 times().
2313 hypothesis[dateutil]
2315 hypothesis[datetime]
2316 This module provides deprecated time and date related strategies.
2317 It depends on the pytz package, which is stable enough that almost any
2319 version should be compatible - most updates are for the timezone data‐
2320 base.
2321 hypothesis.extra.datetime.datetimes(allow_naive=None, timezones=None,
2323 min_year=None, max_year=None)
2324 Return a strategy for generating datetimes.
2325 Deprecated since version 3.9.0: use hypothesis.strategies.date‐
2327 times() instead.
2328 allow_naive=True will cause the values to sometimes be naive.
2331 timezones is the set of permissible timezones. If set to an
2332 empty collection all datetimes will be naive. If set to None all
2333 timezones available via pytz will be used.
2334 All generated datetimes will be between min_year and max_year,
2336 inclusive.
2337 hypothesis.extra.datetime.dates(min_year=None, max_year=None)
2339 Return a strategy for generating dates.
2340 Deprecated since version 3.9.0: use hypothesis.strate‐
2342 gies.dates() instead.
2343 All generated dates will be between min_year and max_year,
2346 inclusive.
2347 hypothesis.extra.datetime.times(allow_naive=None, timezones=None)
2349 Return a strategy for generating times.
2350 Deprecated since version 3.9.0: use hypothesis.strate‐
2352 gies.times() instead.
2353 The allow_naive and timezones arguments act the same as the
2356 datetimes strategy above.
2357 hypothesis[fakefactory]
2359 NOTE:
2360 This extra package is deprecated. We strongly recommend using
2361 native Hypothesis strategies, which are more effective at both find‐
2362 ing and shrinking failing examples for your tests.
2363 The from_regex(), text() (with some specific alphabet), and sam‐
2365 pled_from() strategies may be particularly useful.
2366 Faker (previously fake-factory) is a Python package that generates fake
2368 data for you. It's great for bootstraping your database, creating
2369 good-looking XML documents, stress-testing a database, or anonymizing
2370 production data. However, it's not designed for automated testing -
2371 data from Hypothesis looks less realistic, but produces minimal
2372 bug-triggering examples and uses coverage information to check more
2373 cases.
2374 hypothesis.extra.fakefactory lets you use Faker generators to parame‐
2376 trize Hypothesis tests. This was only ever meant to ease your transi‐
2377 tion to Hypothesis, but we've improved Hypothesis enough since then
2378 that we no longer recommend using Faker for automated tests under any
2379 circumstances.
2380 hypothesis.extra.fakefactory defines a function fake_factory which
2382 returns a strategy for producing text data from any Faker provider.
2383 So for example the following will parametrize a test by an email
2385 address:
2386 >>> fake_factory('email').example()
2388 'tnader@prosacco.info'
2389 >>> fake_factory('name').example()
2391 'Zbyněk Černý CSc.'
2392 You can explicitly specify the locale (otherwise it uses any of the
2394 available locales), either as a single locale or as several:
2395 >>> fake_factory('name', locale='en_GB').example()
2397 'Antione Gerlach'
2398 >>> fake_factory('name', locales=['en_GB', 'cs_CZ']).example()
2399 'Miloš Šťastný'
2400 >>> fake_factory('name', locales=['en_GB', 'cs_CZ']).example()
2401 'Harm Sanford'
2402 You can use custom Faker providers via the providers argument:
2404 >>> from faker.providers import BaseProvider
2406 >>> class KittenProvider(BaseProvider):
2407 ... def meows(self):
2408 ... return 'meow %d' % (self.random_number(digits=10),)
2409 >>> fake_factory('meows', providers=[KittenProvider]).example()
2410 'meow 9139348419'
2411
2413 Hypothesis offers a number of features specific for Django testing,
2414 available in the hypothesis[django] extra. This is tested against each
2415 supported series with mainstream or extended support - if you're still
2416 getting security patches, you can test with Hypothesis.
2417 Using it is quite straightforward: All you need to do is subclass
2419 hypothesis.extra.django.TestCase or hypothesis.extra.django.Transac‐
2420 tionTestCase and you can use @given as normal, and the transactions
2421 will be per example rather than per test function as they would be if
2422 you used @given with a normal django test suite (this is important
2423 because your test function will be called multiple times and you don't
2424 want them to interfere with each other). Test cases on these classes
2425 that do not use @given will be run as normal.
2426 I strongly recommend not using TransactionTestCase unless you really
2428 have to. Because Hypothesis runs this in a loop the performance prob‐
2429 lems it normally has are significantly exacerbated and your tests will
2430 be really slow. If you are using TransactionTestCase, you may need to
2431 use @settings(suppress_health_check=[HealthCheck.too_slow]) to avoid
2432 errors due to slow example generation.
2433 Having set up a test class, you can now pass @given a strategy for
2435 Django models:
2436 For example, using the trivial django project I have for testing:
2438 >>> from hypothesis.extra.django.models import models
2440 >>> from toystore.models import Customer
2441 >>> c = models(Customer).example()
2442 >>> c
2443 <Customer: Customer object>
2444 >>> c.email
2445 'jaime.urbina@gmail.com'
2446 >>> c.name
2447 '\U00109d3d\U000e07be\U000165f8\U0003fabf\U000c12cd\U000f1910\U00059f12\U000519b0\U0003fabf\U000f1910\U000423fb\U000423fb\U00059f12\U000e07be\U000c12cd\U000e07be\U000519b0\U000165f8\U0003fabf\U0007bc31'
2448 >>> c.age
2449 -873375803
2450 Hypothesis has just created this with whatever the relevant type of
2452 data is.
2453 Obviously the customer's age is implausible, which is only possible
2455 because we have not used (eg) MinValueValidator to set the valid range
2456 for this field (or used a PositiveSmallIntegerField, which would only
2457 need a maximum value validator).
2458 If you do have validators attached, Hypothesis will only generate exam‐
2460 ples that pass validation. Sometimes that will mean that we fail a
2461 HealthCheck because of the filtering, so let's explicitly pass a strat‐
2462 egy to skip validation at the strategy level:
2463 NOTE:
2465 Inference from validators will be much more powerful when issue
2466 #1116 is implemented, but there will always be some edge cases that
2467 require you to pass an explicit strategy.
2468 >>> from hypothesis.strategies import integers
2470 >>> c = models(Customer, age=integers(min_value=0, max_value=120)).example()
2471 >>> c
2472 <Customer: Customer object>
2473 >>> c.age
2474 5
2475 Tips and tricks
2477 Custom field types
2478 If you have a custom Django field type you can register it with Hypoth‐
2479 esis's model deriving functionality by registering a default strategy
2480 for it:
2481 >>> from toystore.models import CustomishField, Customish
2483 >>> models(Customish).example()
2484 hypothesis.errors.InvalidArgument: Missing arguments for mandatory field
2485 customish for model Customish
2486 >>> from hypothesis.extra.django.models import add_default_field_mapping
2487 >>> from hypothesis.strategies import just
2488 >>> add_default_field_mapping(CustomishField, just("hi"))
2489 >>> x = models(Customish).example()
2490 >>> x.customish
2491 'hi'
2492 Note that this mapping is on exact type. Subtypes will not inherit it.
2494 Generating child models
2496 For the moment there's no explicit support in hypothesis-django for
2497 generating dependent models. i.e. a Company model will generate no
2498 Shops. However if you want to generate some dependent models as well,
2499 you can emulate this by using the flatmap function as follows:
2500 from hypothesis.strategies import lists, just
2502 def generate_with_shops(company):
2504 return lists(models(Shop, company=just(company))).map(lambda _: company)
2505 company_with_shops_strategy = models(Company).flatmap(generate_with_shops)
2507 Lets unpack what this is doing:
2509 The way flatmap works is that we draw a value from the original strat‐
2511 egy, then apply a function to it which gives us a new strategy. We then
2512 draw a value from that strategy. So in this case we're first drawing a
2513 company, and then we're drawing a list of shops belonging to that com‐
2514 pany: The just strategy is a strategy such that drawing it always pro‐
2515 duces the individual value, so models(Shop, company=just(company)) is a
2516 strategy that generates a Shop belonging to the original company.
2517 So the following code would give us a list of shops all belonging to
2519 the same company:
2520 models(Company).flatmap(lambda c: lists(models(Shop, company=just(c))))
2522 The only difference from this and the above is that we want the com‐
2524 pany, not the shops. This is where the inner map comes in. We build the
2525 list of shops and then throw it away, instead returning the company we
2526 started for. This works because the models that Hypothesis generates
2527 are saved in the database, so we're essentially running the inner
2528 strategy purely for the side effect of creating those children in the
2529 database.
2530 Using default field values
2532 Hypothesis ignores field defaults and always tries to generate values,
2533 even if it doesn't know how to. You can tell it to use the default
2534 value for a field instead of generating one by passing field‐
2535 name=default_value to models():
2536 >>> from toystore.models import DefaultCustomish
2538 >>> models(DefaultCustomish).example()
2539 hypothesis.errors.InvalidArgument: Missing arguments for mandatory field
2540 customish for model DefaultCustomish
2541 >>> from hypothesis.extra.django.models import default_value
2542 >>> x = models(DefaultCustomish, customish=default_value).example()
2543 >>> x.customish
2544 'b'
2545 Generating primary key values
2547 If your model includes a custom primary key that you want to generate
2548 using a strategy (rather than a default auto-increment primary key)
2549 then Hypothesis has to deal with the possibility of a duplicate primary
2550 key.
2551 If a model strategy generates a value for the primary key field,
2553 Hypothesis will create the model instance with update_or_create(),
2554 overwriting any existing instance in the database for this test case
2555 with the same primary key.
2556
2558 numpy
2559 Hypothesis offers a number of strategies for NumPy testing, available
2560 in the hypothesis[numpy] extra. It lives in the hypothesis.extra.numpy
2561 package.
2562 The centerpiece is the arrays() strategy, which generates arrays with
2564 any dtype, shape, and contents you can specify or give a strategy for.
2565 To make this as useful as possible, strategies are provided to generate
2566 array shapes and generate all kinds of fixed-size or compound dtypes.
2567 pandas
2569 Hypothesis provides strategies for several of the core pandas data
2570 types: pandas.Index, pandas.Series and pandas.DataFrame.
2571 The general approach taken by the pandas module is that there are mul‐
2573 tiple strategies for generating indexes, and all of the other strate‐
2574 gies take the number of entries they contain from their index strategy
2575 (with sensible defaults). So e.g. a Series is specified by specifying
2576 its numpy.dtype (and/or a strategy for generating elements for it).
2577 Supported Versions
2579 There is quite a lot of variation between pandas versions. We only com‐
2580 mit to supporting the latest version of pandas, but older minor ver‐
2581 sions are supported on a "best effort" basis. Hypothesis is currently
2582 tested against and confirmed working with Pandas 0.19, 0.20, 0.21, and
2583 0.22.
2584 Releases that are not the latest patch release of their minor version
2586 are not tested or officially supported, but will probably also work
2587 unless you hit a pandas bug.
2588
2590 Hypothesis tries to detect common mistakes and things that will cause
2591 difficulty at run time in the form of a number of 'health checks'.
2592 These include detecting and warning about:
2594 · Strategies with very slow data generation
2596 · Strategies which filter out too much
2598 · Recursive strategies which branch too much
2600 · Tests that are unlikely to complete in a reasonable amount of time.
2602 If any of these scenarios are detected, Hypothesis will emit a warning
2604 about them.
2605 The general goal of these health checks is to warn you about things
2607 that you are doing that might appear to work but will either cause
2608 Hypothesis to not work correctly or to perform badly.
2609 To selectively disable health checks, use the suppress_health_check
2611 setting. The argument for this parameter is a list with elements drawn
2612 from any of the class-level attributes of the HealthCheck class. Using
2613 a value of HealthCheck.all() will disable all health checks.
2614 class hypothesis.HealthCheck
2616 Arguments for suppress_health_check.
2617 Each member of this enum is a type of health check to suppress.
2619 exception_in_generation = 0
2621 Deprecated and no longer does anything. It used to con‐
2622 vert errors in data generation into FailedHealthCheck
2623 error.
2624 data_too_large = 1
2626 Check for when the typical size of the examples you are
2627 generating exceeds the maximum allowed size too often.
2628 filter_too_much = 2
2630 Check for when the test is filtering out too many exam‐
2631 ples, either through use of assume() or filter(), or
2632 occasionally for Hypothesis internal reasons.
2633 too_slow = 3
2635 Check for when your data generation is extremely slow and
2636 likely to hurt testing.
2637 random_module = 4
2639 Deprecated and no longer does anything. It used to check
2640 for whether your tests used the global random module. Now
2641 @given tests automatically seed random so this is no
2642 longer an error.
2643 return_value = 5
2645 Checks if your tests return a non-None value (which will
2646 be ignored and is unlikely to do what you want).
2647 hung_test = 6
2649 Checks if your tests have been running for a very long
2650 time.
2651 large_base_example = 7
2653 Checks if the natural example to shrink towards is very
2654 large.
2655 not_a_test_method = 8
2657 Checks if @given has been applied to a method of
2658 unittest.TestCase.
2659
2661 When Hypothesis finds a bug it stores enough information in its data‐
2662 base to reproduce it. This enables you to have a classic testing work‐
2663 flow of find a bug, fix a bug, and be confident that this is actually
2664 doing the right thing because Hypothesis will start by retrying the
2665 examples that broke things last time.
2666 Limitations
2668 The database is best thought of as a cache that you never need to
2669 invalidate: Information may be lost when you upgrade a Hypothesis ver‐
2670 sion or change your test, so you shouldn't rely on it for correctness -
2671 if there's an example you want to ensure occurs each time then there's
2672 a feature for including them in your source code - but it helps the
2673 development workflow considerably by making sure that the examples
2674 you've just found are reproduced.
2675 The database also records examples that excercise less-used parts of
2677 your code, so the database may update even when no failing examples
2678 were found.
2679 File locations
2681 The default storage format is as a fairly opaque directory structure.
2682 Each test corresponds to a directory, and each example to a file within
2683 that directory. The standard location for it is .hypothesis/examples
2684 in your current working directory. You can override this by setting the
2685 database setting.
2686 There is also a legacy sqlite3 based format. This is mostly still sup‐
2688 ported for compatibility reasons, and support will be dropped in some
2689 future version of Hypothesis. If you use a database file name ending in
2690 .db, .sqlite or .sqlite3 that format will be used instead.
2691 If you have not configured a database and the default location is unus‐
2693 able (e.g. because you do not have read/write permission), Hypothesis
2694 will issue a warning and then fall back to an in-memory database.
2695 Upgrading Hypothesis and changing your tests
2697 The design of the Hypothesis database is such that you can put arbi‐
2698 trary data in the database and not get wrong behaviour. When you
2699 upgrade Hypothesis, old data might be invalidated, but this should hap‐
2700 pen transparently. It should never be the case that e.g. changing the
2701 strategy that generates an argument sometimes gives you data from the
2702 old strategy.
2703 Sharing your example database
2705 NOTE:
2706 If specific examples are important for correctness you should use
2707 the @example decorator, as the example database may discard entries
2708 due to changes in your code or dependencies. For most users, we
2709 therefore recommend using the example database locally and possibly
2710 persisting it between CI builds, but not tracking it under version
2711 control.
2712 The examples database can be shared simply by checking the directory
2714 into version control, for example with the following .gitignore:
2715 # Ignore files cached by Hypothesis...
2717 .hypothesis/*
2718 # except for the examples directory
2719 !.hypothesis/examples/
2720 Like everything under .hypothesis/, the examples directory will be
2722 transparently created on demand. Unlike the other subdirectories,
2723 examples/ is designed to handle merges, deletes, etc if you just add
2724 the directory into git, mercurial, or any similar version control sys‐
2725 tem.
2726
2728 With @given, your tests are still something that you mostly write your‐
2729 self, with Hypothesis providing some data. With Hypothesis's stateful
2730 testing, Hypothesis instead tries to generate not just data but entire
2731 tests. You specify a number of primitive actions that can be combined
2732 together, and then Hypothesis will try to find sequences of those
2733 actions that result in a failure.
2734 NOTE:
2736 This style of testing is often called model-based testing, but in
2737 Hypothesis is called stateful testing (mostly for historical reasons
2738 - the original implementation of this idea in Hypothesis was more
2739 closely based on ScalaCheck's stateful testing where the name is
2740 more apt). Both of these names are somewhat misleading: You don't
2741 really need any sort of formal model of your code to use this, and
2742 it can be just as useful for pure APIs that don't involve any state
2743 as it is for stateful ones.
2744 It's perhaps best to not take the name of this sort of testing too
2746 seriously. Regardless of what you call it, it is a powerful form of
2747 testing which is useful for most non-trivial APIs.
2748 Hypothesis has two stateful testing APIs: A high level one, providing
2750 what we call rule based state machines, and a low level one, providing
2751 what we call generic state machines.
2752 You probably want to use the rule based state machines - they provide a
2754 high level API for describing the sort of actions you want to perform,
2755 based on a structured representation of actions. However the generic
2756 state machines are more flexible, and are particularly useful if you
2757 want the set of currently possible actions to depend primarily on
2758 external state.
2759 Rule based state machines
2761 Rule based state machines are the ones you're most likely to want to
2762 use. They're significantly more user friendly and should be good
2763 enough for most things you'd want to do.
2764 The two main ingredients of a rule based state machine are rules and
2766 bundles.
2767 A rule is very similar to a normal @given based test in that it takes
2769 values drawn from strategies and passes them to a user defined test
2770 function. The key difference is that where @given based tests must be
2771 independent, rules can be chained together - a single test run may
2772 involve multiple rule invocations, which may interact in various ways.
2773 A Bundle is a named collection of generated values that can be reused
2775 by other operations in the test. They are populated with the results
2776 of rules, and may be used as arguments to rules, allowing data to flow
2777 from one rule to another, and rules to work on the results of previous
2778 computations or actions.
2779 The following rule based state machine example is a simplified version
2781 of a test for Hypothesis's example database implementation. An example
2782 database maps keys to sets of values, and in this test we compare one
2783 implementation of it to a simplified in memory model of its behaviour,
2784 which just stores the same values in a Python dict. The test then runs
2785 operations against both the real database and the in-memory representa‐
2786 tion of it and looks for discrepancies in their behaviour.
2787 import shutil
2789 import tempfile
2790 from collections import defaultdict
2792 import hypothesis.strategies as st
2793 from hypothesis.database import DirectoryBasedExampleDatabase
2794 from hypothesis.stateful import Bundle, RuleBasedStateMachine, rule
2795 class DatabaseComparison(RuleBasedStateMachine):
2798 def __init__(self):
2799 super(DatabaseComparison, self).__init__()
2800 self.tempd = tempfile.mkdtemp()
2801 self.database = DirectoryBasedExampleDatabase(self.tempd)
2802 self.model = defaultdict(set)
2803 keys = Bundle('keys')
2805 values = Bundle('values')
2806 @rule(target=keys, k=st.binary())
2808 def k(self, k):
2809 return k
2810 @rule(target=values, v=st.binary())
2812 def v(self, v):
2813 return v
2814 @rule(k=keys, v=values)
2816 def save(self, k, v):
2817 self.model[k].add(v)
2818 self.database.save(k, v)
2819 @rule(k=keys, v=values)
2821 def delete(self, k, v):
2822 self.model[k].discard(v)
2823 self.database.delete(k, v)
2824 @rule(k=keys)
2826 def values_agree(self, k):
2827 assert set(self.database.fetch(k)) == self.model[k]
2828 def teardown(self):
2830 shutil.rmtree(self.tempd)
2831 TestDBComparison = DatabaseComparison.TestCase
2834 In this we declare two bundles - one for keys, and one for values. We
2836 have two trivial rules which just populate them with data (k and v),
2837 and three non-trivial rules: save saves a value under a key and delete
2838 removes a value from a key, in both cases also updating the model of
2839 what should be in the database. values_agree then checks that the con‐
2840 tents of the database agrees with the model for a particular key.
2841 We can then integrate this into our test suite by getting a unittest
2843 TestCase from it:
2844 TestTrees = DatabaseComparison.TestCase
2846 # Or just run with pytest's unittest support
2848 if __name__ == '__main__':
2849 unittest.main()
2850 This test currently passes, but if we comment out the line where we
2852 call self.model[k].discard(v), we would see the following output when
2853 run under pytest:
2854 AssertionError: assert set() == {b''}
2856 ------------ Hypothesis ------------
2858 state = DatabaseComparison()
2860 v1 = state.k(k=b'')
2861 v2 = state.v(v=v1)
2862 state.save(k=v1, v=v2)
2863 state.delete(k=v1, v=v2)
2864 state.values_agree(k=v1)
2865 state.teardown()
2866 Note how it's printed out a very short program that will demonstrate
2868 the problem. The output from a rule based state machine should gener‐
2869 ally be pretty close to Python code - if you have custom repr implemen‐
2870 tations that don't return valid Python then it might not be, but most
2871 of the time you should just be able to copy and paste the code into a
2872 test to reproduce it.
2873 You can control the detailed behaviour with a settings object on the
2875 TestCase (this is a normal hypothesis settings object using the
2876 defaults at the time the TestCase class was first referenced). For
2877 example if you wanted to run fewer examples with larger programs you
2878 could change the settings to:
2879 DatabaseComparison.settings = settings(max_examples=50, stateful_step_count=100)
2881 Which doubles the number of steps each program runs and halves the num‐
2883 ber of test cases that will be run.
2884 Rules
2886 As said earlier, rules are the most common feature used in RuleBased‐
2887 StateMachine. They are defined by applying the rule() decorator on a
2888 function. Note that RuleBasedStateMachine must have at least one rule
2889 defined and that a single function cannot be used to define multiple
2890 rules (this to avoid having multiple rules doing the same things). Due
2891 to the stateful execution method, rules generally cannot take arguments
2892 from other sources such as fixtures or pytest.mark.parametrize - con‐
2893 sider providing them via a strategy such as sampled_from() instead.
2894 hypothesis.stateful.rule(targets=(), target=None, **kwargs)
2896 Decorator for RuleBasedStateMachine. Any name present in target
2897 or targets will define where the end result of this function
2898 should go. If both are empty then the end result will be dis‐
2899 carded.
2900 targets may either be a Bundle or the name of a Bundle.
2902 kwargs then define the arguments that will be passed to the
2904 function invocation. If their value is a Bundle then values that
2905 have previously been produced for that bundle will be provided,
2906 if they are anything else it will be turned into a strategy and
2907 values from that will be provided.
2908 Initializes
2910 Initializes are a special case of rules that are guaranteed to be run
2911 at most once at the beginning of a run (i.e. before any normal rule is
2912 called). Note if multiple initialize rules are defined, they may be
2913 called in any order, and that order will vary from run to run.
2914 Initializes are typically useful to populate bundles:
2916 hypothesis.stateful.initialize(targets=(), target=None, **kwargs)
2918 Decorator for RuleBasedStateMachine.
2919 An initialize decorator behaves like a rule, but the decorated
2921 method is called at most once in a run. All initialize decorated
2922 methods will be called before any rule decorated methods, in an
2923 arbitrary order.
2924 import hypothesis.strategies as st
2926 from hypothesis.stateful import RuleBasedStateMachine, Bundle, rule, initialize
2927 name_strategy = st.text(min_size=1).filter(lambda x: "/" not in x)
2929 class NumberModifier(RuleBasedStateMachine):
2931 folders = Bundle('folders')
2933 files = Bundle('files')
2934 @initialize(target=folders)
2936 def init_folders(self):
2937 return '/'
2938 @rule(target=folders, name=name_strategy)
2940 def create_folder(self, parent, name):
2941 return '%s/%s' % (parent, name)
2942 @rule(target=files, name=name_strategy)
2944 def create_file(self, parent, name):
2945 return '%s/%s' % (parent, name)
2946 Preconditions
2948 While it's possible to use assume() in RuleBasedStateMachine rules, if
2949 you use it in only a few rules you can quickly run into a situation
2950 where few or none of your rules pass their assumptions. Thus, Hypothe‐
2951 sis provides a precondition() decorator to avoid this problem. The
2952 precondition() decorator is used on rule-decorated functions, and must
2953 be given a function that returns True or False based on the RuleBased‐
2954 StateMachine instance.
2955 hypothesis.stateful.precondition(precond)
2957 Decorator to apply a precondition for rules in a RuleBased‐
2958 StateMachine. Specifies a precondition for a rule to be consid‐
2959 ered as a valid step in the state machine. The given function
2960 will be called with the instance of RuleBasedStateMachine and
2961 should return True or False. Usually it will need to look at
2962 attributes on that instance.
2963 For example:
2965 class MyTestMachine(RuleBasedStateMachine):
2967 state = 1
2968 @precondition(lambda self: self.state != 0)
2970 @rule(numerator=integers())
2971 def divide_with(self, numerator):
2972 self.state = numerator / self.state
2973 This is better than using assume in your rule since more valid
2975 rules should be able to be run.
2976 from hypothesis.stateful import RuleBasedStateMachine, rule, precondition
2978 class NumberModifier(RuleBasedStateMachine):
2980 num = 0
2982 @rule()
2984 def add_one(self):
2985 self.num += 1
2986 @precondition(lambda self: self.num != 0)
2988 @rule()
2989 def divide_with_one(self):
2990 self.num = 1 / self.num
2991 By using precondition() here instead of assume(), Hypothesis can filter
2993 the inapplicable rules before running them. This makes it much more
2994 likely that a useful sequence of steps will be generated.
2995 Note that currently preconditions can't access bundles; if you need to
2997 use preconditions, you should store relevant data on the instance
2998 instead.
2999 Invariants
3001 Often there are invariants that you want to ensure are met after every
3002 step in a process. It would be possible to add these as rules that are
3003 run, but they would be run zero or multiple times between other rules.
3004 Hypothesis provides a decorator that marks a function to be run after
3005 every step.
3006 hypothesis.stateful.invariant()
3008 Decorator to apply an invariant for rules in a RuleBasedStateMa‐
3009 chine. The decorated function will be run after every rule and
3010 can raise an exception to indicate failed invariants.
3011 For example:
3013 class MyTestMachine(RuleBasedStateMachine):
3015 state = 1
3016 @invariant()
3018 def is_nonzero(self):
3019 assert self.state != 0
3020 from hypothesis.stateful import RuleBasedStateMachine, rule, invariant
3022 class NumberModifier(RuleBasedStateMachine):
3024 num = 0
3026 @rule()
3028 def add_two(self):
3029 self.num += 2
3030 if self.num > 50:
3031 self.num += 1
3032 @invariant()
3034 def divide_with_one(self):
3035 assert self.num % 2 == 0
3036 NumberTest = NumberModifier.TestCase
3038 Invariants can also have precondition()s applied to them, in which case
3040 they will only be run if the precondition function returns true.
3041 Note that currently invariants can't access bundles; if you need to use
3043 invariants, you should store relevant data on the instance instead.
3044 Generic state machines
3046 The class GenericStateMachine is the underlying machinery of stateful
3047 testing in Hypothesis. Chances are you will want to use the rule based
3048 stateful testing for most things, but the generic state machine func‐
3049 tionality can be useful e.g. if you want to test things where the set
3050 of actions to be taken is more closely tied to the state of the system
3051 you are testing.
3052 class hypothesis.stateful.GenericStateMachine
3054 A GenericStateMachine is the basic entry point into Hypothesis's
3055 approach to stateful testing.
3056 The intent is for it to be subclassed to provide state machine
3058 descriptions
3059 The way this is used is that Hypothesis will repeatedly execute
3061 something that looks something like:
3062 x = MyStatemachineSubclass()
3064 x.check_invariants()
3065 try:
3066 for _ in range(n_steps):
3067 x.execute_step(x.steps().example())
3068 x.check_invariants()
3069 finally:
3070 x.teardown()
3071 And if this ever produces an error it will shrink it down to a
3073 small sequence of example choices demonstrating that.
3074 steps()
3076 Return a SearchStrategy instance the defines the avail‐
3077 able next steps.
3078 execute_step(step)
3080 Execute a step that has been previously drawn from
3081 self.steps()
3082 teardown()
3084 Called after a run has finished executing to clean up any
3085 necessary state.
3086 Does nothing by default.
3088 check_invariants()
3090 Called after initializing and after executing each step.
3091 For example, here we use stateful testing as a sort of link checker, to
3093 test hypothesis.works for broken links or links that use HTTP instead
3094 of HTTPS.
3095 from hypothesis.stateful import GenericStateMachine
3097 import hypothesis.strategies as st
3098 from requests_html import HTMLSession
3099 class LinkChecker(GenericStateMachine):
3102 def __init__(self):
3103 super(LinkChecker, self).__init__()
3104 self.session = HTMLSession()
3105 self.result = None
3106 def steps(self):
3108 if self.result is None:
3109 # Always start on the home page
3110 return st.just("https://hypothesis.works/")
3111 else:
3112 return st.sampled_from([
3113 l
3114 for l in self.result.html.absolute_links
3115 # Don't try to crawl to other people's sites
3116 if l.startswith("https://hypothesis.works") and
3117 # Avoid Cloudflare's bot protection. We are a bot but we don't
3118 # care about the info it's hiding.
3119 '/cdn-cgi/' not in l
3120 ])
3121 def execute_step(self, step):
3123 self.result = self.session.get(step)
3124 assert self.result.status_code == 200
3126 for l in self.result.html.absolute_links:
3128 # All links should be HTTPS
3129 assert "http://hypothesis.works" not in l
3130 TestLinks = LinkChecker.TestCase
3133 Running this (at the time of writing this documentation) produced the
3135 following output:
3136 AssertionError: assert 'http://hypothesis.works' not in 'http://hypoth...test-fixtures/'
3138 'http://hypothesis.works' is contained here:
3139 http://hypothesis.works/articles/hypothesis-pytest-fixtures/
3140 ? +++++++++++++++++++++++
3141 ------------ Hypothesis ------------
3143 Step #1: 'https://hypothesis.works/'
3145 Step #2: 'https://hypothesis.works/articles/'
3146 More fine grained control
3148 If you want to bypass the TestCase infrastructure you can invoke these
3149 manually. The stateful module exposes the function
3150 run_state_machine_as_test, which takes an arbitrary function returning
3151 a GenericStateMachine and an optional settings parameter and does the
3152 same as the class based runTest provided.
3153 In particular this may be useful if you wish to pass parameters to a
3155 custom __init__ in your subclass.
3156
3158 Hypothesis does its level best to be compatible with everything you
3159 could possibly need it to be compatible with. Generally you should just
3160 try it and expect it to work. If it doesn't, you can be surprised and
3161 check this document for the details.
3162 Python versions
3164 Hypothesis is supported and tested on CPython 2.7 and CPython 3.4+.
3165 Hypothesis also supports PyPy2, and will support PyPy3 when there is a
3167 stable release supporting Python 3.4+. Hypothesis does not currently
3168 work on Jython, though it probably could (issue #174). IronPython might
3169 work but hasn't been tested. 32-bit and narrow builds should work,
3170 though this is currently only tested on Windows.
3171 In general Hypothesis does not officially support anything except the
3173 latest patch release of any version of Python it supports. Earlier
3174 releases should work and bugs in them will get fixed if reported, but
3175 they're not tested in CI and no guarantees are made.
3176 Operating systems
3178 In theory Hypothesis should work anywhere that Python does. In practice
3179 it is only known to work and regularly tested on OS X, Windows and
3180 Linux, and you may experience issues running it elsewhere.
3181 If you're using something else and it doesn't work, do get in touch and
3183 I'll try to help, but unless you can come up with a way for me to run a
3184 CI server on that operating system it probably won't stay fixed due to
3185 the inevitable march of time.
3186 Testing frameworks
3188 In general Hypothesis goes to quite a lot of effort to generate things
3189 that look like normal Python test functions that behave as closely to
3190 the originals as possible, so it should work sensibly out of the box
3191 with every test framework.
3192 If your testing relies on doing something other than calling a function
3194 and seeing if it raises an exception then it probably won't work out of
3195 the box. In particular things like tests which return generators and
3196 expect you to do something with them (e.g. nose's yield based tests)
3197 will not work. Use a decorator or similar to wrap the test to take this
3198 form, or ask the framework maintainer to support our hooks for insert‐
3199 ing such a wrapper later.
3200 In terms of what's actually known to work:
3202 · Hypothesis integrates as smoothly with py.test and unittest as we
3204 can make it, and this is verified as part of the CI. Note however
3205 that @given should only be used on tests, not
3206 python:unittest.TestCase setup or teardown methods.
3207 · pytest fixtures work in the usual way for tests that have been
3209 decorated with @given - just avoid passing a strategy for each
3210 argument that will be supplied by a fixture. However, each fix‐
3211 ture will run once for the whole function, not once per example.
3212 Decorating a fixture function is meaningless.
3213 · Nose works fine with hypothesis, and this is tested as part of the
3215 CI. yield based tests simply won't work.
3216 · Integration with Django's testing requires use of the hypothe‐
3218 sis-django package. The issue is that in Django's tests' normal
3219 mode of execution it will reset the database once per test rather
3220 than once per example, which is not what you want.
3221 · Coverage works out of the box with Hypothesis - we use it to guide
3223 example selection for user code, and Hypothesis has 100% branch
3224 coverage in its own tests.
3225 Optional Packages
3227 The supported versions of optional packages, for strategies in hypothe‐
3228 sis.extra, are listed in the documentation for that extra. Our general
3229 goal is to support all versions that are supported upstream.
3230 Regularly verifying this
3232 Everything mentioned above as explicitly supported is checked on every
3233 commit with Travis, Appveyor, and CircleCI. Our continous delivery
3234 pipeline runs all of these checks before publishing each release, so
3235 when we say they're supported we really mean it.
3236 Hypothesis versions
3238 Backwards compatibility is better than backporting fixes, so we use
3239 semantic versioning and only support the most recent version of Hypoth‐
3240 esis. See support for more information.
3241
3243 This is a collection of examples of how to use Hypothesis in interest‐
3244 ing ways. It's small for now but will grow over time.
3245 All of these examples are designed to be run under py.test (nose should
3247 probably work too).
3248 How not to sort by a partial order
3250 The following is an example that's been extracted and simplified from a
3251 real bug that occurred in an earlier version of Hypothesis. The real
3252 bug was a lot harder to find.
3253 Suppose we've got the following type:
3255 class Node(object):
3257 def __init__(self, label, value):
3258 self.label = label
3259 self.value = tuple(value)
3260 def __repr__(self):
3262 return "Node(%r, %r)" % (self.label, self.value)
3263 def sorts_before(self, other):
3265 if len(self.value) >= len(other.value):
3266 return False
3267 return other.value[:len(self.value)] == self.value
3268 Each node is a label and a sequence of some data, and we have the rela‐
3270 tionship sorts_before meaning the data of the left is an initial seg‐
3271 ment of the right. So e.g. a node with value [1, 2] will sort before a
3272 node with value [1, 2, 3], but neither of [1, 2] nor [1, 3] will sort
3273 before the other.
3274 We have a list of nodes, and we want to topologically sort them with
3276 respect to this ordering. That is, we want to arrange the list so that
3277 if x.sorts_before(y) then x appears earlier in the list than y. We
3278 naively think that the easiest way to do this is to extend the partial
3279 order defined here to a total order by breaking ties arbitrarily and
3280 then using a normal sorting algorithm. So we define the following code:
3281 from functools import total_ordering
3283 @total_ordering
3286 class TopoKey(object):
3287 def __init__(self, node):
3288 self.value = node
3289 def __lt__(self, other):
3291 if self.value.sorts_before(other.value):
3292 return True
3293 if other.value.sorts_before(self.value):
3294 return False
3295 return self.value.label < other.value.label
3297 def sort_nodes(xs):
3300 xs.sort(key=TopoKey)
3301 This takes the order defined by sorts_before and extends it by breaking
3303 ties by comparing the node labels.
3304 But now we want to test that it works.
3306 First we write a function to verify that our desired outcome holds:
3308 def is_prefix_sorted(xs):
3310 for i in range(len(xs)):
3311 for j in range(i+1, len(xs)):
3312 if xs[j].sorts_before(xs[i]):
3313 return False
3314 return True
3315 This will return false if it ever finds a pair in the wrong order and
3317 return true otherwise.
3318 Given this function, what we want to do with Hypothesis is assert that
3320 for all sequences of nodes, the result of calling sort_nodes on it is
3321 sorted.
3322 First we need to define a strategy for Node:
3324 from hypothesis import settings, strategies
3326 import hypothesis.strategies as s
3327 NodeStrategy = s.builds(
3329 Node,
3330 s.integers(),
3331 s.lists(s.booleans(), average_size=5, max_size=10))
3332 We want to generate short lists of values so that there's a decent
3334 chance of one being a prefix of the other (this is also why the choice
3335 of bool as the elements). We then define a strategy which builds a node
3336 out of an integer and one of those short lists of booleans.
3337 We can now write a test:
3339 from hypothesis import given
3341 @given(s.lists(NodeStrategy))
3343 def test_sorting_nodes_is_prefix_sorted(xs):
3344 sort_nodes(xs)
3345 assert is_prefix_sorted(xs)
3346 this immediately fails with the following example:
3348 [Node(0, (False, True)), Node(0, (True,)), Node(0, (False,))]
3350 The reason for this is that because False is not a prefix of (True,
3352 True) nor vice versa, sorting things the first two nodes are equal
3353 because they have equal labels. This makes the whole order non-transi‐
3354 tive and produces basically nonsense results.
3355 But this is pretty unsatisfying. It only works because they have the
3357 same label. Perhaps we actually wanted our labels to be unique. Lets
3358 change the test to do that.
3359 def deduplicate_nodes_by_label(nodes):
3361 table = {node.label: node for node in nodes}
3362 return list(table.values())
3363 We define a function to deduplicate nodes by labels, and can now map
3365 that over a strategy for lists of nodes to give us a strategy for lists
3366 of nodes with unique labels:
3367 @given(s.lists(NodeStrategy).map(deduplicate_nodes_by_label))
3369 def test_sorting_nodes_is_prefix_sorted(xs):
3370 sort_nodes(xs)
3371 assert is_prefix_sorted(xs)
3372 Hypothesis quickly gives us an example of this still being wrong:
3374 [Node(0, (False,)), Node(-1, (True,)), Node(-2, (False, False))])
3376 Now this is a more interesting example. None of the nodes will sort
3378 equal. What is happening here is that the first node is strictly less
3379 than the last node because (False,) is a prefix of (False, False). This
3380 is in turn strictly less than the middle node because neither is a pre‐
3381 fix of the other and -2 < -1. The middle node is then less than the
3382 first node because -1 < 0.
3383 So, convinced that our implementation is broken, we write a better one:
3385 def sort_nodes(xs):
3387 for i in hrange(1, len(xs)):
3388 j = i - 1
3389 while j >= 0:
3390 if xs[j].sorts_before(xs[j+1]):
3391 break
3392 xs[j], xs[j+1] = xs[j+1], xs[j]
3393 j -= 1
3394 This is just insertion sort slightly modified - we swap a node back‐
3396 wards until swapping it further would violate the order constraints.
3397 The reason this works is because our order is a partial order already
3398 (this wouldn't produce a valid result for a general topological sorting
3399 - you need the transitivity).
3400 We now run our test again and it passes, telling us that this time
3402 we've successfully managed to sort some nodes without getting it com‐
3403 pletely wrong. Go us.
3404 Time zone arithmetic
3406 This is an example of some tests for pytz which check that various
3407 timezone conversions behave as you would expect them to. These tests
3408 should all pass, and are mostly a demonstration of some useful sorts of
3409 thing to test with Hypothesis, and how the datetimes() strategy works.
3410 >>> from datetime import timedelta
3412 >>> from hypothesis.extra.pytz import timezones
3413 >>> from hypothesis.strategies import datetimes
3414 >>> # The datetimes strategy is naive by default, so tell it to use timezones
3416 >>> aware_datetimes = datetimes(timezones=timezones())
3417 >>> @given(aware_datetimes, timezones(), timezones())
3419 ... def test_convert_via_intermediary(dt, tz1, tz2):
3420 ... """Test that converting between timezones is not affected
3421 ... by a detour via another timezone.
3422 ... """
3423 ... assert dt.astimezone(tz1).astimezone(tz2) == dt.astimezone(tz2)
3424 >>> @given(aware_datetimes, timezones())
3426 ... def test_convert_to_and_fro(dt, tz2):
3427 ... """If we convert to a new timezone and back to the old one
3428 ... this should leave the result unchanged.
3429 ... """
3430 ... tz1 = dt.tzinfo
3431 ... assert dt == dt.astimezone(tz2).astimezone(tz1)
3432 >>> @given(aware_datetimes, timezones())
3434 ... def test_adding_an_hour_commutes(dt, tz):
3435 ... """When converting between timezones it shouldn't matter
3436 ... if we add an hour here or add an hour there.
3437 ... """
3438 ... an_hour = timedelta(hours=1)
3439 ... assert (dt + an_hour).astimezone(tz) == dt.astimezone(tz) + an_hour
3440 >>> @given(aware_datetimes, timezones())
3442 ... def test_adding_a_day_commutes(dt, tz):
3443 ... """When converting between timezones it shouldn't matter
3444 ... if we add a day here or add a day there.
3445 ... """
3446 ... a_day = timedelta(days=1)
3447 ... assert (dt + a_day).astimezone(tz) == dt.astimezone(tz) + a_day
3448 >>> # And we can check that our tests pass
3450 >>> test_convert_via_intermediary()
3451 >>> test_convert_to_and_fro()
3452 >>> test_adding_an_hour_commutes()
3453 >>> test_adding_a_day_commutes()
3454 Condorcet's Paradox
3456 A classic paradox in voting theory, called Condorcet's paradox, is that
3457 majority preferences are not transitive. That is, there is a population
3458 and a set of three candidates A, B and C such that the majority of the
3459 population prefer A to B, B to C and C to A.
3460 Wouldn't it be neat if we could use Hypothesis to provide an example of
3462 this?
3463 Well as you can probably guess from the presence of this section, we
3465 can! This is slightly surprising because it's not really obvious how we
3466 would generate an election given the types that Hypothesis knows about.
3467 The trick here turns out to be twofold:
3469 1. We can generate a type that is much larger than an election, extract
3471 an election out of that, and rely on minimization to throw away all
3472 the extraneous detail.
3473 2. We can use assume and rely on Hypothesis's adaptive exploration to
3475 focus on the examples that turn out to generate interesting elec‐
3476 tions
3477 Without further ado, here is the code:
3479 from hypothesis import given, assume
3481 from hypothesis.strategies import integers, lists
3482 from collections import Counter
3483 def candidates(votes):
3486 return {candidate for vote in votes for candidate in vote}
3487 def build_election(votes):
3490 """
3491 Given a list of lists we extract an election out of this. We do this
3492 in two phases:
3493 1. First of all we work out the full set of candidates present in all
3495 votes and throw away any votes that do not have that whole set.
3496 2. We then take each vote and make it unique, keeping only the first
3497 instance of any candidate.
3498 This gives us a list of total orderings of some set. It will usually
3500 be a lot smaller than the starting list, but that's OK.
3501 """
3502 all_candidates = candidates(votes)
3503 votes = list(filter(lambda v: set(v) == all_candidates, votes))
3504 if not votes:
3505 return []
3506 rebuilt_votes = []
3507 for vote in votes:
3508 rv = []
3509 for v in vote:
3510 if v not in rv:
3511 rv.append(v)
3512 assert len(rv) == len(all_candidates)
3513 rebuilt_votes.append(rv)
3514 return rebuilt_votes
3515 @given(lists(lists(integers(min_value=1, max_value=5))))
3518 def test_elections_are_transitive(election):
3519 election = build_election(election)
3520 # Small elections are unlikely to be interesting
3521 assume(len(election) >= 3)
3522 all_candidates = candidates(election)
3523 # Elections with fewer than three candidates certainly can't exhibit
3524 # intransitivity
3525 assume(len(all_candidates) >= 3)
3526 # Now we check if the election is transitive
3528 # First calculate the pairwise counts of how many prefer each candidate
3530 # to the other
3531 counts = Counter()
3532 for vote in election:
3533 for i in range(len(vote)):
3534 for j in range(i+1, len(vote)):
3535 counts[(vote[i], vote[j])] += 1
3536 # Now look at which pairs of candidates one has a majority over the
3538 # other and store that.
3539 graph = {}
3540 all_candidates = candidates(election)
3541 for i in all_candidates:
3542 for j in all_candidates:
3543 if counts[(i, j)] > counts[(j, i)]:
3544 graph.setdefault(i, set()).add(j)
3545 # Now for each triple assert that it is transitive.
3547 for x in all_candidates:
3548 for y in graph.get(x, ()):
3549 for z in graph.get(y, ()):
3550 assert x not in graph.get(z, ())
3551 The example Hypothesis gives me on my first run (your mileage may of
3553 course vary) is:
3554 [[3, 1, 4], [4, 3, 1], [1, 4, 3]]
3556 Which does indeed do the job: The majority (votes 0 and 1) prefer 3 to
3558 1, the majority (votes 0 and 2) prefer 1 to 4 and the majority (votes 1
3559 and 2) prefer 4 to 3. This is in fact basically the canonical example
3560 of the voting paradox, modulo variations on the names of candidates.
3561 Fuzzing an HTTP API
3563 Hypothesis's support for testing HTTP services is somewhat nascent.
3564 There are plans for some fully featured things around this, but right
3565 now they're probably quite far down the line.
3566 But you can do a lot yourself without any explicit support! Here's a
3568 script I wrote to throw random data against the API for an entirely
3569 fictitious service called Waspfinder (this is only lightly obfuscated
3570 and you can easily figure out who I'm actually talking about, but I
3571 don't want you to run this code and hammer their API without their per‐
3572 mission).
3573 All this does is use Hypothesis to generate random JSON data matching
3575 the format their API asks for and check for 500 errors. More advanced
3576 tests which then use the result and go on to do other things are defi‐
3577 nitely also possible. The swagger-conformance package provides an
3578 excellent example of this!
3579 import unittest
3581 from hypothesis import given, assume, settings, strategies as st
3582 from collections import namedtuple
3583 import requests
3584 import os
3585 import random
3586 import time
3587 import math
3588 # These tests will be quite slow because we have to talk to an external
3590 # service. Also we'll put in a sleep between calls so as to not hammer it.
3591 # As a result we reduce the number of test cases and turn off the timeout.
3592 settings.default.max_examples = 100
3593 settings.default.timeout = -1
3594 Goal = namedtuple("Goal", ("slug",))
3596 # We just pass in our API credentials via environment variables.
3599 waspfinder_token = os.getenv('WASPFINDER_TOKEN')
3600 waspfinder_user = os.getenv('WASPFINDER_USER')
3601 assert waspfinder_token is not None
3602 assert waspfinder_user is not None
3603 GoalData = st.fixed_dictionaries({
3605 'title': st.text(),
3606 'goal_type': st.sampled_from([
3607 "hustler", "biker", "gainer", "fatloser", "inboxer",
3608 "drinker", "custom"]),
3609 'goaldate': st.one_of(st.none(), st.floats()),
3610 'goalval': st.one_of(st.none(), st.floats()),
3611 'rate': st.one_of(st.none(), st.floats()),
3612 'initval': st.floats(),
3613 'panic': st.floats(),
3614 'secret': st.booleans(),
3615 'datapublic': st.booleans(),
3616 })
3617 needs2 = ['goaldate', 'goalval', 'rate']
3620 class WaspfinderTest(unittest.TestCase):
3623 @given(GoalData)
3625 def test_create_goal_dry_run(self, data):
3626 # We want slug to be unique for each run so that multiple test runs
3627 # don't interfere with each other. If for some reason some slugs trigger
3628 # an error and others don't we'll get a Flaky error, but that's OK.
3629 slug = hex(random.getrandbits(32))[2:]
3630 # Use assume to guide us through validation we know about, otherwise
3632 # we'll spend a lot of time generating boring examples.
3633 # Title must not be empty
3635 assume(data["title"])
3636 # Exactly two of these values should be not None. The other will be
3638 # inferred by the API.
3639 assume(len([1 for k in needs2 if data[k] is not None]) == 2)
3641 for v in data.values():
3642 if isinstance(v, float):
3643 assume(not math.isnan(v))
3644 data["slug"] = slug
3645 # The API nicely supports a dry run option, which means we don't have
3647 # to worry about the user account being spammed with lots of fake goals
3648 # Otherwise we would have to make sure we cleaned up after ourselves
3649 # in this test.
3650 data["dryrun"] = True
3651 data["auth_token"] = waspfinder_token
3652 for d, v in data.items():
3653 if v is None:
3654 data[d] = "null"
3655 else:
3656 data[d] = str(v)
3657 result = requests.post(
3658 "https://waspfinder.example.com/api/v1/users/"
3659 "%s/goals.json" % (waspfinder_user,), data=data)
3660 # Lets not hammer the API too badly. This will of course make the
3662 # tests even slower than they otherwise would have been, but that's
3663 # life.
3664 time.sleep(1.0)
3665 # For the moment all we're testing is that this doesn't generate an
3667 # internal error. If we didn't use the dry run option we could have
3668 # then tried doing more with the result, but this is a good start.
3669 self.assertNotEqual(result.status_code, 500)
3670 if __name__ == '__main__':
3672 unittest.main()
3673
3675 The Hypothesis community is small for the moment but is full of excel‐
3676 lent people who can answer your questions and help you out. Please do
3677 join us.
3678 The two major places for community discussion are:
3680 · The mailing list.
3682 · An IRC channel, #hypothesis on freenode, which is more active than
3684 the mailing list.
3685 Feel free to use these to ask for help, provide feedback, or discuss
3687 anything remotely Hypothesis related at all.
3688 Please note that the Hypothesis code of conduct applies in all Hypothe‐
3690 sis community spaces.
3691 If you would like to cite Hypothesis, please consider our sugested
3693 citation.
3694 If you like repo badges, we suggest the following badge, which you can
3696 add with reStructuredText or Markdown, respectively: [image]
3697 .. image:: https://img.shields.io/badge/hypothesis-tested-brightgreen.svg
3699 :alt: Tested with Hypothesis
3700 :target: https://hypothesis.readthedocs.io
3701 [](https://hypothesis.readthedocs.io/)
3703
3705 What is Hypothesis for?
3706 From the perspective of a user, the purpose of Hypothesis is to make it
3708 easier for you to write better tests.
3709 From my perspective as the author, that is of course also a purpose of
3711 Hypothesis, but (if you will permit me to indulge in a touch of megalo‐
3712 mania for a moment), the larger purpose of Hypothesis is to drag the
3713 world kicking and screaming into a new and terrifying age of high qual‐
3714 ity software.
3715 Software is, as they say, eating the world. Software is also terrible.
3717 It's buggy, insecure and generally poorly thought out. This combination
3718 is clearly a recipe for disaster.
3719 And the state of software testing is even worse. Although it's fairly
3721 uncontroversial at this point that you should be testing your code, can
3722 you really say with a straight face that most projects you've worked on
3723 are adequately tested?
3724 A lot of the problem here is that it's too hard to write good tests.
3726 Your tests encode exactly the same assumptions and fallacies that you
3727 had when you wrote the code, so they miss exactly the same bugs that
3728 you missed when you wrote the code.
3729 Meanwhile, there are all sorts of tools for making testing better that
3731 are basically unused. The original Quickcheck is from 1999 and the
3732 majority of developers have not even heard of it, let alone used it.
3733 There are a bunch of half-baked implementations for most languages, but
3734 very few of them are worth using.
3735 The goal of Hypothesis is to bring advanced testing techniques to the
3737 masses, and to provide an implementation that is so high quality that
3738 it is easier to use them than it is not to use them. Where I can, I
3739 will beg, borrow and steal every good idea I can find that someone has
3740 had to make software testing better. Where I can't, I will invent new
3741 ones.
3742 Quickcheck is the start, but I also plan to integrate ideas from fuzz
3744 testing (a planned future feature is to use coverage information to
3745 drive example selection, and the example saving database is already
3746 inspired by the workflows people use for fuzz testing), and am open to
3747 and actively seeking out other suggestions and ideas.
3748 The plan is to treat the social problem of people not using these ideas
3750 as a bug to which there is a technical solution: Does property-based
3751 testing not match your workflow? That's a bug, let's fix it by figur‐
3752 ing out how to integrate Hypothesis into it. Too hard to generate cus‐
3753 tom data for your application? That's a bug. Let's fix it by figuring
3754 out how to make it easier, or how to take something you're already
3755 using to specify your data and derive a generator from that automati‐
3756 cally. Find the explanations of these advanced ideas hopelessly obtuse
3757 and hard to follow? That's a bug. Let's provide you with an easy API
3758 that lets you test your code better without a PhD in software verifica‐
3759 tion.
3760 Grand ambitions, I know, and I expect ultimately the reality will be
3762 somewhat less grand, but so far in about three months of development,
3763 Hypothesis has become the most solid implementation of Quickcheck ever
3764 seen in a mainstream language (as long as we don't count Scala as main‐
3765 stream yet), and at the same time managed to significantly push forward
3766 the state of the art, so I think there's reason to be optimistic.
3767
3769 This is a page for listing people who are using Hypothesis and how
3770 excited they are about that. If that's you and your name is not on the
3771 list, this file is in Git and I'd love it if you sent me a pull request
3772 to fix that.
3773 Stripe
3775 At Stripe we use Hypothesis to test every piece of our machine learning
3776 model training pipeline (powered by scikit). Before we migrated, our
3777 tests were filled with hand-crafted pandas Dataframes that weren't rep‐
3778 resentative at all of our actual very complex data. Because we needed
3779 to craft examples for each test, we took the easy way out and lived
3780 with extremely low test coverage.
3781 Hypothesis changed all that. Once we had our strategies for generating
3783 Dataframes of features it became trivial to slightly customize each
3784 strategy for new tests. Our coverage is now close to 90%.
3785 Full-stop, property-based testing is profoundly more powerful - and has
3787 caught or prevented far more bugs - than our old style of example-based
3788 testing.
3789 Kristian Glass - Director of Technology at LaterPay GmbH
3791 Hypothesis has been brilliant for expanding the coverage of our test
3792 cases, and also for making them much easier to read and understand, so
3793 we're sure we're testing the things we want in the way we want.
3794 Seth Morton
3796 When I first heard about Hypothesis, I knew I had to include it in my
3797 two open-source Python libraries, natsort and fastnumbers . Quite
3798 frankly, I was a little appalled at the number of bugs and "holes" I
3799 found in the code. I can now say with confidence that my libraries are
3800 more robust to "the wild." In addition, Hypothesis gave me the confi‐
3801 dence to expand these libraries to fully support Unicode input, which I
3802 never would have had the stomach for without such thorough testing
3803 capabilities. Thanks!
3804 Sixty North
3806 At Sixty North we use Hypothesis for testing Segpy an open source
3807 Python library for shifting data between Python data structures and SEG
3808 Y files which contain geophysical data from the seismic reflection sur‐
3809 veys used in oil and gas exploration.
3810 This is our first experience of property-based testing – as opposed to
3812 example-based testing. Not only are our tests more powerful, they are
3813 also much better explanations of what we expect of the production code.
3814 In fact, the tests are much closer to being specifications. Hypothesis
3815 has located real defects in our code which went undetected by tradi‐
3816 tional test cases, simply because Hypothesis is more relentlessly devi‐
3817 ous about test case generation than us mere humans! We found Hypothe‐
3818 sis particularly beneficial for Segpy because SEG Y is an antiquated
3819 format that uses legacy text encodings (EBCDIC) and even a legacy
3820 floating point format we implemented from scratch in Python.
3821 Hypothesis is sure to find a place in most of our future Python code‐
3823 bases and many existing ones too.
3824 mulkieran
3826 Just found out about this excellent QuickCheck for Python implementa‐
3827 tion and ran up a few tests for my bytesize package last night. Refuted
3828 a few hypotheses in the process.
3829 Looking forward to using it with a bunch of other projects as well.
3831 Adam Johnson
3833 I have written a small library to serialize dicts to MariaDB's dynamic
3834 columns binary format, mariadb-dyncol. When I first developed it, I
3835 thought I had tested it really well - there were hundreds of test
3836 cases, some of them even taken from MariaDB's test suite itself. I was
3837 ready to release.
3838 Lucky for me, I tried Hypothesis with David at the PyCon UK sprints.
3840 Wow! It found bug after bug after bug. Even after a first release, I
3841 thought of a way to make the tests do more validation, which revealed a
3842 further round of bugs! Most impressively, Hypothesis found a compli‐
3843 cated off-by-one error in a condition with 4095 versus 4096 bytes of
3844 data - something that I would never have found.
3845 Long live Hypothesis! (Or at least, property-based testing).
3847 Josh Bronson
3849 Adopting Hypothesis improved bidict's test coverage and significantly
3850 increased our ability to make changes to the code with confidence that
3851 correct behavior would be preserved. Thank you, David, for the great
3852 testing tool.
3853 Cory Benfield
3855 Hypothesis is the single most powerful tool in my toolbox for working
3856 with algorithmic code, or any software that produces predictable output
3857 from a wide range of sources. When using it with Priority, Hypothesis
3858 consistently found errors in my assumptions and extremely subtle bugs
3859 that would have taken months of real-world use to locate. In some
3860 cases, Hypothesis found subtle deviations from the correct output of
3861 the algorithm that may never have been noticed at all.
3862 When it comes to validating the correctness of your tools, nothing
3864 comes close to the thoroughness and power of Hypothesis.
3865 Jon Moore
3867 One extremely satisfied user here. Hypothesis is a really solid imple‐
3868 mentation of property-based testing, adapted well to Python, and with
3869 good features such as failure-case shrinkers. I first used it on a
3870 project where we needed to verify that a vendor's Python and non-Python
3871 implementations of an algorithm matched, and it found about a dozen
3872 cases that previous example-based testing and code inspections had not.
3873 Since then I've been evangelizing for it at our firm.
3874 Russel Winder
3876 I am using Hypothesis as an integral part of my Python workshops. Test‐
3877 ing is an integral part of Python programming and whilst unittest and,
3878 better, py.test can handle example-based testing, property-based test‐
3879 ing is increasingly far more important than example-base testing, and
3880 Hypothesis fits the bill.
3881 Wellfire Interactive
3883 We've been using Hypothesis in a variety of client projects, from test‐
3884 ing Django-related functionality to domain-specific calculations. It
3885 both speeds up and simplifies the testing process since there's so much
3886 less tedious and error-prone work to do in identifying edge cases. Test
3887 coverage is nice but test depth is even nicer, and it's much easier to
3888 get meaningful test depth using Hypothesis.
3889 Cody Kochmann
3891 Hypothesis is being used as the engine for random object generation
3892 with my open source function fuzzer battle_tested which maps all behav‐
3893 iors of a function allowing you to minimize the chance of unexpected
3894 crashes when running code in production.
3895 With how efficient Hypothesis is at generating the edge cases that
3897 cause unexpected behavior occur, battle_tested is able to map out the
3898 entire behavior of most functions in less than a few seconds.
3899 Hypothesis truly is a masterpiece. I can't thank you enough for build‐
3901 ing it.
3902 Merchise Autrement
3904 Just minutes after our first use of hypothesis we uncovered a subtle
3905 bug in one of our most used library. Since then, we have increasingly
3906 used hypothesis to improve the quality of our testing in libraries and
3907 applications as well.
3908 Florian Kromer
3910 At Roboception GmbH I use Hypothesis to implement fully automated
3911 stateless and stateful reliability tests for the 3D sensor rc_visard
3912 and robotic software components .
3913 Thank you very much for creating the (probably) most powerful prop‐
3915 erty-based testing framework.
3916 Your name goes here
3918 I know there are many more, because I keep finding out about new people
3919 I'd never even heard of using Hypothesis. If you're looking to way to
3920 give back to a tool you love, adding your name here only takes a moment
3921 and would really help a lot. As per instructions at the top, just send
3922 me a pull request and I'll add you to the list.
3923
3925 The following is a non-exhaustive list of open source projects I know
3926 are using Hypothesis. If you're aware of any others please add them to
3927 the list! The only inclusion criterion right now is that if it's a
3928 Python library then it should be available on pypi.
3929 You can find hundreds more from the Hypothesis page at libraries.io,
3931 and over a thousand on GitHub.
3932 · aur
3934 · argon2_cffi
3936 · attrs
3938 · axelrod
3940 · bidict
3942 · binaryornot
3944 · brotlipy
3946 · chardet
3948 · cmph-cffi
3950 · cryptography
3952 · dbus-signature-pyparsing
3954 · fastnumbers
3956 · flocker
3958 · flownetpy
3960 · funsize
3962 · fusion-index
3964 · hyper-h2
3966 · into-dbus-python
3968 · justbases
3970 · justbytes
3972 · loris
3974 · mariadb-dyncol
3976 · mercurial
3978 · natsort
3980 · pretext
3982 · priority
3984 · PyCEbox
3986 · PyPy
3988 · pyrsistent
3990 · python-humble-utils
3992 · pyudev
3994 · qutebrowser
3996 · RubyMarshal
3998 · Segpy
4000 · simoa
4002 · srt
4004 · tchannel
4006 · vdirsyncer
4008 · wcag-contrast-ratio
4010 · yacluster
4012 · yturl
4014
4016 Hypothesis has been eagerly used and extended by the open source commu‐
4017 nity. This page lists extensions and applications; you can find more
4018 or newer packages by searching PyPI or libraries.io.
4019 If there's something missing which you think should be here, let us
4021 know!
4022 NOTE:
4024 Being listed on this page does not imply that the Hypothesis main‐
4025 tainers endorse a package.
4026 External Strategies
4028 Some packages provide strategies directly:
4029 · hypothesis-fspaths - strategy to generate filesystem paths.
4031 · hypothesis-geojson - strategy to generate GeoJson.
4033 · hs-dbus-signature - strategy to generate arbitrary D-Bus signatures.
4035 · hypothesis_sqlalchemy - strategies to generate SQLAlchemy objects.
4037 · hypothesis-ros - strategies to generate messages and parameters for
4039 the Robot Operating System.
4040 Others provide a function to infer a strategy from some other schema:
4042 · lollipop-hypothesis - infer strategies from lollipop schemas.
4044 · hypothesis-drf - infer strategies from a djangorestframework seri‐
4046 aliser.
4047 · hypothesis-mongoengine - infer strategies from a mongoengine model.
4049 · hypothesis-pb - infer strategies from Protocol Buffer schemas.
4051 Other Cool Things
4053 swagger-conformance is powered by Hypothesis and pyswagger. Based on a
4054 Swagger specification, it can build and run an entire test suite to
4055 check that the implementation matches the spec. The command-line ver‐
4056 sion can test apps written in any language, simply by passing the file
4057 or URL path to the schema to check!
4058 Trio is an async framework with "an obsessive focus on usability and
4060 correctness", so naturally it works with Hypothesis! pytest-trio
4061 includes a custom hook that allows @given(...) to work with Trio-style
4062 async test functions, and hypothesis-trio includes stateful testing
4063 extensions to support concurrent programs.
4064 libarchimedes makes it easy to use Hypothesis in the Hy language, a
4066 Lisp embedded in Python.
4067 battle_tested is a fuzzing tool that will show you how your code can
4069 fail - by trying all kinds of inputs and reporting whatever happens.
4070 pytest-subtesthack functions as a workaround for issue #377.
4072 Writing an Extension
4074 See CONTRIBUTING.rst for more information.
4075 New strategies can be added to Hypothesis, or published as an external
4077 package on PyPI - either is fine for most strategies. If in doubt, ask!
4078 It's generally much easier to get things working outside, because
4080 there's more freedom to experiment and fewer requirements in stability
4081 and API style. We're happy to review and help with external packages as
4082 well as pull requests!
4083 If you're thinking about writing an extension, please name it hypothe‐
4085 sis-{something} - a standard prefix makes the community more visible
4086 and searching for extensions easier.
4087 On the other hand, being inside gets you access to some deeper imple‐
4089 mentation features (if you need them) and better long-term guarantees
4090 about maintenance. We particularly encourage pull requests for new
4091 composable primitives that make implementing other strategies easier,
4092 or for widely used types in the standard library. Strategies for other
4093 things are also welcome; anything with external dependencies just goes
4094 in hypothesis.extra.
4095
4097 This is a record of all past Hypothesis releases and what went into
4098 them, in reverse chronological order. All previous releases should
4099 still be available on pip.
4100 Hypothesis APIs come in three flavours:
4102 · Public: Hypothesis releases since 1.0 are semantically versioned with
4104 respect to these parts of the API. These will not break except
4105 between major version bumps. All APIs mentioned in this documentation
4106 are public unless explicitly noted otherwise.
4107 · Semi-public: These are APIs that are considered ready to use but are
4109 not wholly nailed down yet. They will not break in patch releases and
4110 will usually not break in minor releases, but when necessary minor
4111 releases may break semi-public APIs.
4112 · Internal: These may break at any time and you really should not use
4114 them at all.
4115 You should generally assume that an API is internal unless you have
4117 specific information to the contrary.
4118 3.66.11 - 2018-07-28
4120 This patch modifies how which rule to run is selected during rule based
4121 stateful testing. This should result in a slight performance increase
4122 during generation and a significant performance and quality improvement
4123 when shrinking.
4124 As a result of this change, some state machines which would previously
4126 have thrown an InvalidDefinition are no longer detected as invalid.
4127 3.66.10 - 2018-07-28
4129 This release weakens some minor functionality in the shrinker that had
4130 only modest benefit and made its behaviour much harder to reason about.
4131 This is unlikely to have much user visible effect, but it is possible
4133 that in some cases shrinking may get slightly slower. It is primarily
4134 to make it easier to work on the shrinker and pave the way for future
4135 work.
4136 3.66.9 - 2018-07-26
4138 This release improves the information that Hypothesis emits about its
4139 shrinking when verbosity is set to debug.
4140 3.66.8 - 2018-07-24
4142 This patch includes some minor fixes in the documentation, and updates
4143 the minimum version of pytest to 3.0 (released August 2016).
4144 3.66.7 - 2018-07-24
4146 This release fixes a bug where difficult to shrink tests could some‐
4147 times trigger an internal assertion error inside the shrinker.
4148 3.66.6 - 2018-07-23
4150 This patch ensures that Hypothesis fully supports Python 3.7, by
4151 upgrading from_type() (issue #1264) and fixing some minor issues in our
4152 test suite (issue #1148).
4153 3.66.5 - 2018-07-22
4155 This patch fixes the online docs for various extras, by ensuring that
4156 their dependencies are installed on readthedocs.io (issue #1326).
4157 3.66.4 - 2018-07-20
4159 This release improves the shrinker's ability to reorder examples.
4160 For example, consider the following test:
4162 import hypothesis.strategies as st
4164 from hypothesis import given
4165 @given(st.text(), st.text())
4167 def test_non_equal(x, y):
4168 assert x != y
4169 Previously this could have failed with either of x="", y="0" or x="0",
4171 y="". Now it should always fail with x="", y="0".
4172 This will allow the shrinker to produce more consistent results, espe‐
4174 cially in cases where test cases contain some ordered collection whose
4175 actual order does not matter.
4176 3.66.3 - 2018-07-20
4178 This patch fixes inference in the builds() strategy with subtypes of
4179 python:typing.NamedTuple, where the __init__ method is not useful for
4180 introspection. We now use the field types instead - thanks to James
4181 Uther for identifying this bug.
4182 3.66.2 - 2018-07-19
4184 This release improves the shrinker's ability to handle situations where
4185 there is an additive constraint between two values.
4186 For example, consider the following test:
4188 import hypothesis.strategies as st
4190 from hypothesis import given
4191 @given(st.integers(), st.integers())
4193 def test_does_not_exceed_100(m, n):
4194 assert m + n < 100
4195 Previously this could have failed with almost any pair (m, n) with 0 <=
4197 m <= n and m + n == 100. Now it should almost always fail with m=0,
4198 n=100.
4199 This is a relatively niche specialisation, but can be useful in situa‐
4201 tions where e.g. a bug is triggered by an integer overflow.
4202 3.66.1 - 2018-07-09
4204 This patch fixes a rare bug where an incorrect percentage drawtime
4205 could be displayed for a test, when the system clock was changed during
4206 a test running under Python 2 (we use python:time.monotonic() where it
4207 is available to avoid such problems). It also fixes a possible
4208 zero-division error that can occur when the underlying C library dou‐
4209 ble-rounds an intermediate value in python:math.fsum() and gets the
4210 least significant bit wrong.
4211 3.66.0 - 2018-07-05
4213 This release improves validation of the alphabet argument to the text()
4214 strategy. The following misuses are now deprecated, and will be an
4215 error in a future version:
4216 · passing an unordered collection (such as set('abc')), which violates
4218 invariants about shrinking and reproducibility
4219 · passing an alphabet sequence with elements that are not strings
4221 · passing an alphabet sequence with elements that are not of length
4223 one, which violates any size constraints that may apply
4224 Thanks to Sushobhit for adding these warnings (issue #1329).
4226 3.65.3 - 2018-07-04
4228 This release fixes a mostly theoretical bug where certain usage of the
4229 internal API could trigger an assertion error inside Hypothesis. It is
4230 unlikely that this problem is even possible to trigger through the pub‐
4231 lic API.
4232 3.65.2 - 2018-07-04
4234 This release fixes dependency information for coverage. Previously
4235 Hypothesis would allow installing coverage with any version, but it
4236 only works with coverage 4.0 or later.
4237 We now specify the correct metadata in our setup.py, so Hypothesis will
4239 only allow installation with compatible versions of coverage.
4240 3.65.1 - 2018-07-03
4242 This patch ensures that stateful tests which raise an error from a
4243 pytest helper still print the sequence of steps taken to reach that
4244 point (issue #1372). This reporting was previously broken because the
4245 helpers inherit directly from BaseException, and therefore require spe‐
4246 cial handling to catch without breaking e.g. the use of ctrl-C to quit
4247 the test.
4248 3.65.0 - 2018-06-30
4250 This release deprecates the max_shrinks setting in favor of an internal
4251 heuristic. If you need to avoid shrinking examples, use the phases
4252 setting instead. (issue #1235)
4253 3.64.2 - 2018-06-27
4255 This release fixes a bug where an internal assertion error could some‐
4256 times be triggered while shrinking a failing test.
4257 3.64.1 - 2018-06-27
4259 This patch fixes type-checking errors in our vendored pretty-printer,
4260 which were ignored by our mypy config but visible for anyone else
4261 (whoops). Thanks to Pi Delport for reporting issue #1359 so promptly.
4262 3.64.0 - 2018-06-26
4264 This release adds an interface which can be used to insert a wrapper
4265 between the original test function and @given (issue #1257). This will
4266 be particularly useful for test runner extensions such as pytest-trio,
4267 but is not recommended for direct use by other users of Hypothesis.
4268 3.63.0 - 2018-06-26
4270 This release adds a new mechanism to infer strategies for classes
4271 defined using attrs, based on the the type, converter, or validator of
4272 each attribute. This inference is now built in to builds() and
4273 from_type().
4274 On Python 2, from_type() no longer generates instances of int when
4276 passed long, or vice-versa.
4277 3.62.0 - 2018-06-26
4279 This release adds PEP 484 type hints to Hypothesis on a provisional
4280 basis, using the comment-based syntax for Python 2 compatibility. You
4281 can read more about our type hints here.
4282 It also adds the py.typed marker specified in PEP 561. After you pip
4284 install hypothesis, mypy 0.590 or later will therefore type-check your
4285 use of our public interface!
4286 3.61.0 - 2018-06-24
4288 This release deprecates the use of settings as a context manager, the
4289 use of which is somewhat ambiguous.
4290 Users should define settings with global state or with the @set‐
4292 tings(...) decorator.
4293 3.60.1 - 2018-06-20
4295 Fixed a bug in generating an instance of a Django model from a strategy
4296 where the primary key is generated as part of the strategy. See details
4297 here.
4298 Thanks to Tim Martin for this contribution.
4300 3.60.0 - 2018-06-20
4302 This release add initialize decorator for stateful testing (originally
4303 discussed in issue #1216). initialize act as a special rule that is
4304 only called once, and all initialize rules are guaranteed to be called
4305 before any normal rule is called.
4306 3.59.3 - 2018-06-19
4308 This is a no-op release to take into account some changes to the
4309 release process. It should have no user visible effect.
4310 3.59.2 - 2018-06-18
4312 This adds support for partially sorting examples which cannot be fully
4313 sorted. For example, [5, 4, 3, 2, 1, 0] with a constraint that the
4314 first element needs to be larger than the last becomes [1, 2, 3, 4, 5,
4315 0].
4316 Thanks to Luke for contributing.
4318 3.59.1 - 2018-06-16
4320 This patch uses python:random.getstate() and python:random.setstate()
4321 to restore the PRNG state after @given runs deterministic tests. With‐
4322 out restoring state, you might have noticed problems such as issue
4323 #1266. The fix also applies to stateful testing (issue #702).
4324 3.59.0 - 2018-06-14
4326 This release adds the emails() strategy, which generates unicode
4327 strings representing an email address.
4328 Thanks to Sushobhit for moving this to the public API (issue #162).
4330 3.58.1 - 2018-06-13
4332 This improves the shrinker. It can now reorder examples: 3 1 2 becomes
4333 1 2 3.
4334 Thanks to Luke for contributing.
4336 3.58.0 - 2018-06-13
4338 This adds a new extra timezones() strategy that generates dateutil
4339 timezones.
4340 Depends on python-dateutil.
4342 Thanks to Conrad for contributing.
4344 3.57.0 - 2018-05-20
4346 Using an unordered collection with the permutations() strategy has been
4347 deprecated because the order in which e.g. a set shrinks is arbitrary.
4348 This may cause different results between runs.
4349 3.56.10 - 2018-05-16
4351 This release makes define_setting a private method, which has the
4352 effect of hiding it from the documentation.
4353 3.56.9 - 2018-05-11
4355 This is another release with no functionality changes as part of
4356 changes to Hypothesis's new release tagging scheme.
4357 3.56.8 - 2018-05-10
4359 This is a release with no functionality changes that moves Hypothesis
4360 over to a new release tagging scheme.
4361 3.56.7 - 2018-05-10
4363 This release provides a performance improvement for most tests, but in
4364 particular users of sampled_from who don't have numpy installed should
4365 see a significant performance improvement.
4366 3.56.6 - 2018-05-09
4368 This patch contains further internal work to support Mypy. There are
4369 no user-visible changes... yet.
4370 3.56.5 - 2018-04-22
4372 This patch contains some internal refactoring to run mypy in CI. There
4373 are no user-visible changes.
4374 3.56.4 - 2018-04-21
4376 This release involves some very minor internal clean up and should have
4377 no user visible effect at all.
4378 3.56.3 - 2018-04-20
4380 This release fixes a problem introduced in 3.56.0 where setting the
4381 hypothesis home directory (through currently undocumented means) would
4382 no longer result in the default database location living in the new
4383 home directory.
4384 3.56.2 - 2018-04-20
4386 This release fixes a problem introduced in 3.56.0 where setting
4387 max_examples to 1 would result in tests failing with Unsatisfiable.
4388 This problem could also occur in other harder to trigger circumstances
4389 (e.g. by setting it to a low value, having a hard to satisfy assump‐
4390 tion, and disabling health checks).
4391 3.56.1 - 2018-04-20
4393 This release fixes a problem that was introduced in 3.56.0: Use of the
4394 HYPOTHESIS_VERBOSITY_LEVEL environment variable was, rather than depre‐
4395 cated, actually broken due to being read before various setup the dep‐
4396 recation path needed was done. It now works correctly (and emits a dep‐
4397 recation warning).
4398 3.56.0 - 2018-04-17
4400 This release deprecates several redundant or internally oriented set‐
4401 tings, working towards an orthogonal set of configuration options that
4402 are widely useful without requiring any knowledge of our internals (‐
4403 issue #535).
4404 · Deprecated settings that no longer have any effect are no longer
4406 shown in the __repr__ unless set to a non-default value.
4407 · perform_health_check is deprecated, as it duplicates sup‐
4409 press_health_check.
4410 · max_iterations is deprecated and disabled, because we can usually get
4412 better behaviour from an internal heuristic than a user-controlled
4413 setting.
4414 · min_satisfying_examples is deprecated and disabled, due to overlap
4416 with the filter_too_much healthcheck and poor interaction with
4417 max_examples.
4418 · HYPOTHESIS_VERBOSITY_LEVEL is now deprecated. Set verbosity through
4420 the profile system instead.
4421 · Examples tried by find() are now reported at debug verbosity level
4423 (as well as verbose level).
4424 3.55.6 - 2018-04-14
4426 This release fixes a somewhat obscure condition (issue #1230) under
4427 which you could occasionally see a failing test trigger an assertion
4428 error inside Hypothesis instead of failing normally.
4429 3.55.5 - 2018-04-14
4431 This patch fixes one possible cause of issue #966. When running Python
4432 2 with hash randomisation, passing a python:bytes object to python:ran‐
4433 dom.seed() would use version=1, which broke derandomize (because the
4434 seed depended on a randomised hash). If derandomize is still nondeter‐
4435 ministic for you, please open an issue.
4436 3.55.4 - 2018-04-13
4438 This patch makes a variety of minor improvements to the documentation,
4439 and improves a few validation messages for invalid inputs.
4440 3.55.3 - 2018-04-12
4442 This release updates the URL metadata associated with the PyPI package
4443 (again). It has no other user visible effects.
4444 3.55.2 - 2018-04-11
4446 This release updates the URL metadata associated with the PyPI package.
4447 It has no other user visible effects.
4448 3.55.1 - 2018-04-06
4450 This patch relaxes constraints on the expected values returned by the
4451 standard library function hypot() and the internal helper function
4452 cathetus(), this to fix near-exact test-failures on some 32-bit sys‐
4453 tems.
4454 3.55.0 - 2018-04-05
4456 This release includes several improvements to the handling of the data‐
4457 base setting.
4458 · The database_file setting was a historical artefact, and you should
4460 just use database directly.
4461 · The HYPOTHESIS_DATABASE_FILE environment variable is deprecated, in
4463 favor of load_profile() and the database setting.
4464 · If you have not configured the example database at all and the
4466 default location is not usable (due to e.g. permissions issues),
4467 Hypothesis will fall back to an in-memory database. This is not per‐
4468 sisted between sessions, but means that the defaults work on
4469 read-only filesystems.
4470 3.54.0 - 2018-04-04
4472 This release improves the complex_numbers() strategy, which now sup‐
4473 ports min_magnitude and max_magnitude arguments, along with allow_nan
4474 and allow_infinity like for floats().
4475 Thanks to J.J. Green for this feature.
4477 3.53.0 - 2018-04-01
4479 This release removes support for Django 1.8, which reached end of life
4480 on 2018-04-01. You can see Django's release and support schedule on
4481 the Django Project website.
4482 3.52.3 - 2018-04-01
4484 This patch fixes the min_satisfying_examples settings documentation, by
4485 explaining that example shrinking is tracked at the level of the under‐
4486 lying bytestream rather than the output value.
4487 The output from find() in verbose mode has also been adjusted - see the
4489 example session - to avoid duplicating lines when the example repr is
4490 constant, even if the underlying representation has been shrunken.
4491 3.52.2 - 2018-03-30
4493 This release improves the output of failures with rule based stateful
4494 testing in two ways:
4495 · The output from it is now usually valid Python code.
4497 · When the same value has two different names because it belongs to two
4499 different bundles, it will now display with the name associated with
4500 the correct bundle for a rule argument where it is used.
4501 3.52.1 - 2018-03-29
4503 This release improves the behaviour of stateful testing in two ways:
4504 · Previously some runs would run no steps (issue #376). This should no
4506 longer happen.
4507 · RuleBasedStateMachine tests which used bundles extensively would
4509 often shrink terribly. This should now be significantly improved,
4510 though there is likely a lot more room for improvement.
4511 This release also involves a low level change to how ranges of integers
4513 are handles which may result in other improvements to shrink quality in
4514 some cases.
4515 3.52.0 - 2018-03-24
4517 This release deprecates use of @settings(...) as a decorator, on func‐
4518 tions or methods that are not also decorated with @given. You can
4519 still apply these decorators in any order, though you should only do so
4520 once each.
4521 Applying @given twice was already deprecated, and applying @set‐
4523 tings(...) twice is deprecated in this release and will become an error
4524 in a future version. Neither could ever be used twice to good effect.
4525 Using @settings(...) as the sole decorator on a test is completely
4527 pointless, so this common usage error will become an error in a future
4528 version of Hypothesis.
4529 3.51.0 - 2018-03-24
4531 This release deprecates the average_size argument to lists() and other
4532 collection strategies. You should simply delete it wherever it was
4533 used in your tests, as it no longer has any effect.
4534 In early versions of Hypothesis, the average_size argument was treated
4536 as a hint about the distribution of examples from a strategy. Subse‐
4537 quent improvements to the conceptual model and the engine for generat‐
4538 ing and shrinking examples mean it is more effective to simply describe
4539 what constitutes a valid example, and let our internals handle the dis‐
4540 tribution.
4541 3.50.3 - 2018-03-24
4543 This patch contains some internal refactoring so that we can run with
4544 warnings as errors in CI.
4545 3.50.2 - 2018-03-20
4547 This has no user-visible changes except one slight formatting change to
4548 one docstring, to avoid a deprecation warning.
4549 3.50.1 - 2018-03-20
4551 This patch fixes an internal error introduced in 3.48.0, where a check
4552 for the Django test runner would expose import-time errors in Django
4553 configuration (issue #1167).
4554 3.50.0 - 2018-03-19
4556 This release improves validation of numeric bounds for some strategies.
4557 · integers() and floats() now raise InvalidArgument if passed a
4559 min_value or max_value which is not an instance of Real, instead of
4560 various internal errors.
4561 · floats() now converts its bounding values to the nearest float above
4563 or below the min or max bound respectively, instead of just casting
4564 to float. The old behaviour was incorrect in that you could generate
4565 float(min_value), even when this was less than min_value itself (pos‐
4566 sible with eg. fractions).
4567 · When both bounds are provided to floats() but there are no floats in
4569 the interval, such as [(2**54)+1 .. (2**55)-1], InvalidArgument is
4570 raised.
4571 · decimals() gives a more useful error message if passed a string that
4573 cannot be converted to Decimal in a context where this error is not
4574 trapped.
4575 Code that previously seemed to work may be explicitly broken if there
4577 were no floats between min_value and max_value (only possible with
4578 non-float bounds), or if a bound was not a Real number but still
4579 allowed in python:math.isnan (some custom classes with a __float__
4580 method).
4581 3.49.1 - 2018-03-15
4583 This patch fixes our tests for Numpy dtype strategies on big-endian
4584 platforms, where the strategy behaved correctly but the test assumed
4585 that the native byte order was little-endian.
4586 There is no user impact unless you are running our test suite on
4588 big-endian platforms. Thanks to Graham Inggs for reporting issue
4589 #1164.
4590 3.49.0 - 2018-03-12
4592 This release deprecates passing elements=None to collection strategies,
4593 such as lists().
4594 Requiring lists(nothing()) or builds(list) instead of lists() means
4596 slightly more typing, but also improves the consistency and discover‐
4597 ability of our API - as well as showing how to compose or construct
4598 strategies in ways that still work in more complex situations.
4599 Passing a nonzero max_size to a collection strategy where the elements
4601 strategy contains no values is now deprecated, and will be an error in
4602 a future version. The equivalent with elements=None is already an
4603 error.
4604 3.48.1 - 2018-03-05
4606 This patch will minimize examples that would come out non-minimal in
4607 previous versions. Thanks to Kyle Reeve for this patch.
4608 3.48.0 - 2018-03-05
4610 This release improves some "unhappy paths" when using Hypothesis with
4611 the standard library python:unittest module:
4612 · Applying @given to a non-test method which is overridden from
4614 python:unittest.TestCase, such as setUp, raises a new health check.
4615 (issue #991)
4616 · Using subTest() within a test decorated with @given would leak inter‐
4618 mediate results when tests were run under the python:unittest test
4619 runner. Individual reporting of failing subtests is now disabled
4620 during a test using @given. (issue #1071)
4621 · @given is still not a class decorator, but the error message if you
4623 try using it on a class has been improved.
4624 As a related improvement, using django:django.test.TestCase with @given
4626 instead of hypothesis.extra.django.TestCase raises an explicit error
4627 instead of running all examples in a single database transaction.
4628 3.47.0 - 2018-03-02
4630 register_profile now accepts keyword arguments for specific settings,
4631 and the parent settings object is now optional. Using a name for a
4632 registered profile which is not a string was never suggested, but it is
4633 now also deprecated and will eventually be an error.
4634 3.46.2 - 2018-03-01
4636 This release removes an unnecessary branch from the code, and has no
4637 user-visible impact.
4638 3.46.1 - 2018-03-01
4640 This changes only the formatting of our docstrings and should have no
4641 user-visible effects.
4642 3.46.0 - 2018-02-26
4644 characters() has improved docs about what arguments are valid, and
4645 additional validation logic to raise a clear error early (instead of
4646 e.g. silently ignoring a bad argument). Categories may be specified as
4647 the Unicode 'general category' (eg u'Nd'), or as the 'major category'
4648 (eg [u'N', u'Lu'] is equivalent to [u'Nd', u'Nl', u'No', u'Lu']).
4649 In previous versions, general categories were supported and all other
4651 input was silently ignored. Now, major categories are supported in
4652 addition to general categories (which may change the behaviour of some
4653 existing code), and all other input is deprecated.
4654 3.45.5 - 2018-02-26
4656 This patch improves strategy inference in hypothesis.extra.django to
4657 account for some validators in addition to field type - see issue #1116
4658 for ongoing work in this space.
4659 Specifically, if a CharField or TextField has an attached RegexValida‐
4661 tor, we now use from_regex() instead of text() as the underlying strat‐
4662 egy. This allows us to generate examples of the default User model,
4663 closing issue #1112.
4664 3.45.4 - 2018-02-25
4666 This patch improves some internal debugging information, fixes a typo
4667 in a validation error message, and expands the documentation for new
4668 contributors.
4669 3.45.3 - 2018-02-23
4671 This patch may improve example shrinking slightly for some strategies.
4672 3.45.2 - 2018-02-18
4674 This release makes our docstring style more consistent, thanks to
4675 flake8-docstrings. There are no user-visible changes.
4676 3.45.1 - 2018-02-17
4678 This fixes an indentation issue in docstrings for datetimes(), dates(),
4679 times(), and timedeltas().
4680 3.45.0 - 2018-02-13
4682 This release fixes builds() so that target can be used as a keyword
4683 argument for passing values to the target. The target itself can still
4684 be specified as a keyword argument, but that behavior is now depre‐
4685 cated. The target should be provided as the first positional argument.
4686 3.44.26 - 2018-02-06
4688 This release fixes some formatting issues in the Hypothesis source
4689 code. It should have no externally visible effects.
4690 3.44.25 - 2018-02-05
4692 This release changes the way in which Hypothesis tries to shrink the
4693 size of examples. It probably won't have much impact, but might make
4694 shrinking faster in some cases. It is unlikely but not impossible that
4695 it will change the resulting examples.
4696 3.44.24 - 2018-01-27
4698 This release fixes dependency information when installing Hypothesis
4699 from a binary "wheel" distribution.
4700 · The install_requires for enum34 is resolved at install time, rather
4702 than at build time (with potentially different results).
4703 · Django has fixed their python_requires for versions 2.0.0 onward,
4705 simplifying Python2-compatible constraints for downstream projects.
4706 3.44.23 - 2018-01-24
4708 This release improves shrinking in a class of pathological examples
4709 that you are probably never hitting in practice. If you are hitting
4710 them in practice this should be a significant speed up in shrinking. If
4711 you are not, you are very unlikely to notice any difference. You might
4712 see a slight slow down and/or slightly better falsifying examples.
4713 3.44.22 - 2018-01-23
4715 This release fixes a dependency problem. It was possible to install
4716 Hypothesis with an old version of attrs, which would throw a TypeError
4717 as soon as you tried to import hypothesis. Specifically, you need
4718 attrs 16.0.0 or newer.
4719 Hypothesis will now require the correct version of attrs when
4721 installing.
4722 3.44.21 - 2018-01-22
4724 This change adds some additional structural information that Hypothesis
4725 will use to guide its search.
4726 You mostly shouldn't see much difference from this. The two most likely
4728 effects you would notice are:
4729 1. Hypothesis stores slightly more examples in its database for passing
4731 tests.
4732 2. Hypothesis may find new bugs that it was previously missing, but it
4734 probably won't (this is a basic implementation of the feature that
4735 is intended to support future work. Although it is useful on its
4736 own, it's not very useful on its own).
4737 3.44.20 - 2018-01-21
4739 This is a small refactoring release that changes how Hypothesis tracks
4740 some information about the boundary of examples in its internal repre‐
4741 sentation.
4742 You are unlikely to see much difference in behaviour, but memory usage
4744 and run time may both go down slightly during normal test execution,
4745 and when failing Hypothesis might print its failing example slightly
4746 sooner.
4747 3.44.19 - 2018-01-21
4749 This changes how we compute the default average_size for all collection
4750 strategies. Previously setting a max_size without setting an aver‐
4751 age_size would have the seemingly paradoxical effect of making data
4752 generation slower, because it would raise the average size from its
4753 default. Now setting max_size will either leave the default unchanged
4754 or lower it from its default.
4755 If you are currently experiencing this problem, this may make your
4757 tests substantially faster. If you are not, this will likely have no
4758 effect on you.
4759 3.44.18 - 2018-01-20
4761 This is a small refactoring release that changes how Hypothesis detects
4762 when the structure of data generation depends on earlier values gener‐
4763 ated (e.g. when using flatmap or composite()). It should not have any
4764 observable effect on behaviour.
4765 3.44.17 - 2018-01-15
4767 This release fixes a typo in internal documentation, and has no
4768 user-visible impact.
4769 3.44.16 - 2018-01-13
4771 This release improves test case reduction for recursive data struc‐
4772 tures. Hypothesis now guarantees that whenever a strategy calls itself
4773 recursively (usually this will happen because you are using
4774 deferred()), any recursive call may replace the top level value. e.g.
4775 given a tree structure, Hypothesis will always try replacing it with a
4776 subtree.
4777 Additionally this introduces a new heuristic that may in some circum‐
4779 stances significantly speed up test case reduction - Hypothesis should
4780 be better at immediately replacing elements drawn inside another strat‐
4781 egy with their minimal possible value.
4782 3.44.15 - 2018-01-13
4784 from_type() can now resolve recursive types such as binary trees (issue
4785 #1004). Detection of non-type arguments has also improved, leading to
4786 better error messages in many cases involving forward references.
4787 3.44.14 - 2018-01-08
4789 This release fixes a bug in the shrinker that prevented the optimisa‐
4790 tions in 3.44.6 from working in some cases. It would not have worked
4791 correctly when filtered examples were nested (e.g. with a set of inte‐
4792 gers in some range).
4793 This would not have resulted in any correctness problems, but shrinking
4795 may have been slower than it otherwise could be.
4796 3.44.13 - 2018-01-08
4798 This release changes the average bit length of values drawn from inte‐
4799 gers() to be much smaller. Additionally it changes the shrinking order
4800 so that now size is considered before sign - e.g. -1 will be preferred
4801 to +10.
4802 The new internal format for integers required some changes to the mini‐
4804 mizer to make work well, so you may also see some improvements to exam‐
4805 ple quality in unrelated areas.
4806 3.44.12 - 2018-01-07
4808 This changes Hypothesis's internal implementation of weighted sampling.
4809 This will affect example distribution and quality, but you shouldn't
4810 see any other effects.
4811 3.44.11 - 2018-01-06
4813 This is a change to some internals around how Hypothesis handles avoid‐
4814 ing generating duplicate examples and seeking out novel regions of the
4815 search space.
4816 You are unlikely to see much difference as a result of it, but it fixes
4818 a bug where an internal assertion could theoretically be triggered and
4819 has some minor effects on the distribution of examples so could poten‐
4820 tially find bugs that have previously been missed.
4821 3.44.10 - 2018-01-06
4823 This patch avoids creating debug statements when debugging is disabled.
4824 Profiling suggests this is a 5-10% performance improvement (issue
4825 #1040).
4826 3.44.9 - 2018-01-06
4828 This patch blacklists null characters ('\x00') in automatically created
4829 strategies for Django CharField and TextField, due to a database issue
4830 which was recently fixed upstream (Hypothesis issue #1045).
4831 3.44.8 - 2018-01-06
4833 This release makes the Hypothesis shrinker slightly less greedy in
4834 order to avoid local minima - when it gets stuck, it makes a small
4835 attempt to search around the final example it would previously have
4836 returned to find a new starting point to shrink from. This should
4837 improve example quality in some cases, especially ones where the test
4838 data has dependencies among parts of it that make it difficult for
4839 Hypothesis to proceed.
4840 3.44.7 - 2018-01-04
4842 This release adds support for Django 2 in the hypothesis-django extra.
4843 This release drops support for Django 1.10, as it is no longer sup‐
4845 ported by the Django team.
4846 3.44.6 - 2018-01-02
4848 This release speeds up test case reduction in many examples by being
4849 better at detecting large shrinks it can use to discard redundant parts
4850 of its input. This will be particularly noticeable in examples that
4851 make use of filtering and for some integer ranges.
4852 3.44.5 - 2018-01-02
4854 Happy new year!
4855 This is a no-op release that updates the year range on all of the copy‐
4857 right headers in our source to include 2018.
4858 3.44.4 - 2017-12-23
4860 This release fixes issue #1044, which slowed tests by up to 6% due to
4861 broken caching.
4862 3.44.3 - 2017-12-21
4864 This release improves the shrinker in cases where examples drawn ear‐
4865 lier can affect how much data is drawn later (e.g. when you draw a
4866 length parameter in a composite and then draw that many elements).
4867 Examples found in cases like this should now be much closer to minimal.
4868 3.44.2 - 2017-12-20
4870 This is a pure refactoring release which changes how Hypothesis manages
4871 its set of examples internally. It should have no externally visible
4872 effects.
4873 3.44.1 - 2017-12-18
4875 This release fixes issue #997, in which under some circumstances the
4876 body of tests run under Hypothesis would not show up when run under
4877 coverage even though the tests were run and the code they called out‐
4878 side of the test file would show up normally.
4879 3.44.0 - 2017-12-17
4881 This release adds a new feature: The @reproduce_failure decorator,
4882 designed to make it easy to use Hypothesis's binary format for examples
4883 to reproduce a problem locally without having to share your example
4884 database between machines.
4885 This also changes when seeds are printed:
4887 · They will no longer be printed for normal falsifying examples, as
4889 there are now adequate ways of reproducing those for all cases, so it
4890 just contributes noise.
4891 · They will once again be printed when reusing examples from the data‐
4893 base, as health check failures should now be more reliable in this
4894 scenario so it will almost always work in this case.
4895 This work was funded by Smarkets.
4897 3.43.1 - 2017-12-17
4899 This release fixes a bug with Hypothesis's database management - exam‐
4900 ples that were found in the course of shrinking were saved in a way
4901 that indicated that they had distinct causes, and so they would all be
4902 retried on the start of the next test. The intended behaviour, which is
4903 now what is implemented, is that only a bounded subset of these exam‐
4904 ples would be retried.
4905 3.43.0 - 2017-12-17
4907 HypothesisDeprecationWarning now inherits from python:FutureWarning
4908 instead of python:DeprecationWarning, as recommended by PEP 565 for
4909 user-facing warnings (issue #618). If you have not changed the default
4910 warnings settings, you will now see each distinct HypothesisDepreca‐
4911 tionWarning instead of only the first.
4912 3.42.2 - 2017-12-12
4914 This patch fixes issue #1017, where instances of a list or tuple sub‐
4915 type used as an argument to a strategy would be coerced to tuple.
4916 3.42.1 - 2017-12-10
4918 This release has some internal cleanup, which makes reading the code
4919 more pleasant and may shrink large examples slightly faster.
4920 3.42.0 - 2017-12-09
4922 This release deprecates faker-extra, which was designed as a transition
4923 strategy but does not support example shrinking or coverage-guided dis‐
4924 covery.
4925 3.41.0 - 2017-12-06
4927 sampled_from() can now sample from one-dimensional numpy ndarrays. Sam‐
4928 pling from multi-dimensional ndarrays still results in a deprecation
4929 warning. Thanks to Charlie Tanksley for this patch.
4930 3.40.1 - 2017-12-04
4932 This release makes two changes:
4933 · It makes the calculation of some of the metadata that Hypothesis uses
4935 for shrinking occur lazily. This should speed up performance of test
4936 case generation a bit because it no longer calculates information it
4937 doesn't need.
4938 · It improves the shrinker for certain classes of nested examples. e.g.
4940 when shrinking lists of lists, the shrinker is now able to concate‐
4941 nate two adjacent lists together into a single list. As a result of
4942 this change, shrinking may get somewhat slower when the minimal exam‐
4943 ple found is large.
4944 3.40.0 - 2017-12-02
4946 This release improves how various ways of seeding Hypothesis interact
4947 with the example database:
4948 · Using the example database with seed() is now deprecated. You should
4950 set database=None if you are doing that. This will only warn if you
4951 actually load examples from the database while using @seed.
4952 · The derandomize will behave the same way as @seed.
4954 · Using --hypothesis-seed will disable use of the database.
4956 · If a test used examples from the database, it will not suggest using
4958 a seed to reproduce it, because that won't work.
4959 This work was funded by Smarkets.
4961 3.39.0 - 2017-12-01
4963 This release adds a new health check that checks if the smallest "natu‐
4964 ral" possible example of your test case is very large - this will tend
4965 to cause Hypothesis to generate bad examples and be quite slow.
4966 This work was funded by Smarkets.
4968 3.38.9 - 2017-11-29
4970 This is a documentation release to improve the documentation of shrink‐
4971 ing behaviour for Hypothesis's strategies.
4972 3.38.8 - 2017-11-29
4974 This release improves the performance of characters() when using black‐
4975 list_characters and from_regex() when using negative character classes.
4976 The problems this fixes were found in the course of work funded by
4978 Smarkets.
4979 3.38.7 - 2017-11-29
4981 This is a patch release for from_regex(), which had a bug in handling
4982 of the python:re.VERBOSE flag (issue #992). Flags are now handled cor‐
4983 rectly when parsing regex.
4984 3.38.6 - 2017-11-28
4986 This patch changes a few byte-string literals from double to single
4987 quotes, thanks to an update in unify. There are no user-visible
4988 changes.
4989 3.38.5 - 2017-11-23
4991 This fixes the repr of strategies using lambda that are defined inside
4992 decorators to include the lambda source.
4993 This would mostly have been visible when using the statistics function‐
4995 ality - lambdas used for e.g. filtering would have shown up with a
4996 <unknown> as their body. This can still happen, but it should happen
4997 less often now.
4998 3.38.4 - 2017-11-22
5000 This release updates the reported statistics so that they show approxi‐
5001 mately what fraction of your test run time is spent in data generation
5002 (as opposed to test execution).
5003 This work was funded by Smarkets.
5005 3.38.3 - 2017-11-21
5007 This is a documentation release, which ensures code examples are up to
5008 date by running them as doctests in CI (issue #711).
5009 3.38.2 - 2017-11-21
5011 This release changes the behaviour of the deadline setting when used
5012 with data(): Time spent inside calls to data.draw will no longer be
5013 counted towards the deadline time.
5014 As a side effect of some refactoring required for this work, the way
5016 flaky tests are handled has changed slightly. You are unlikely to see
5017 much difference from this, but some error messages will have changed.
5018 This work was funded by Smarkets.
5020 3.38.1 - 2017-11-21
5022 This patch has a variety of non-user-visible refactorings, removing
5023 various minor warts ranging from indirect imports to typos in comments.
5024 3.38.0 - 2017-11-18
5026 This release overhauls the health check system in a variety of small
5027 ways. It adds no new features, but is nevertheless a minor release
5028 because it changes which tests are likely to fail health checks.
5029 The most noticeable effect is that some tests that used to fail health
5031 checks will now pass, and some that used to pass will fail. These
5032 should all be improvements in accuracy. In particular:
5033 · New failures will usually be because they are now taking into account
5035 things like use of data() and assume() inside the test body.
5036 · New failures may also be because for some classes of example the way
5038 data generation performance was measured was artificially faster than
5039 real data generation (for most examples that are hitting performance
5040 health checks the opposite should be the case).
5041 · Tests that used to fail health checks and now pass do so because the
5043 health check system used to run in a way that was subtly different
5044 than the main Hypothesis data generation and lacked some of its sup‐
5045 port for e.g. large examples.
5046 If your data generation is especially slow, you may also see your tests
5048 get somewhat faster, as there is no longer a separate health check
5049 phase. This will be particularly noticeable when rerunning test fail‐
5050 ures.
5051 This work was funded by Smarkets.
5053 3.37.0 - 2017-11-12
5055 This is a deprecation release for some health check related features.
5056 The following are now deprecated:
5058 · Passing exception_in_generation to suppress_health_check. This no
5060 longer does anything even when passed - All errors that occur during
5061 data generation will now be immediately reraised rather than going
5062 through the health check mechanism.
5063 · Passing random_module to suppress_health_check. This hasn't done any‐
5065 thing for a long time, but was never explicitly deprecated. Hypothe‐
5066 sis always seeds the random module when running @given tests, so this
5067 is no longer an error and suppressing it doesn't do anything.
5068 · Passing non-HealthCheck values in suppress_health_check. This was
5070 previously allowed but never did anything useful.
5071 In addition, passing a non-iterable value as suppress_health_check will
5073 now raise an error immediately (it would never have worked correctly,
5074 but it would previously have failed later). Some validation error mes‐
5075 sages have also been updated.
5076 This work was funded by Smarkets.
5078 3.36.1 - 2017-11-10
5080 This is a yak shaving release, mostly concerned with our own tests.
5081 While getfullargspec() was documented as deprecated in Python 3.5, it
5083 never actually emitted a warning. Our code to silence this (nonexis‐
5084 tent) warning has therefore been removed.
5085 We now run our tests with DeprecationWarning as an error, and made some
5087 minor changes to our own tests as a result. This required similar
5088 upstream updates to coverage and execnet (a test-time dependency via
5089 pytest-xdist).
5090 There is no user-visible change in Hypothesis itself, but we encourage
5092 you to consider enabling deprecations as errors in your own tests.
5093 3.36.0 - 2017-11-06
5095 This release adds a setting to the public API, and does some internal
5096 cleanup:
5097 · The derandomize setting is now documented (issue #890)
5099 · Removed - and disallowed - all 'bare excepts' in Hypothesis (issue
5101 #953)
5102 · Documented the strict setting as deprecated, and updated the build so
5104 our docs always match deprecations in the code.
5105 3.35.0 - 2017-11-06
5107 This minor release supports constraining uuids() to generate a particu‐
5108 lar version of UUID (issue #721).
5109 Thanks to Dion Misic for this feature.
5111 3.34.1 - 2017-11-02
5113 This patch updates the documentation to suggest builds(callable)
5114 instead of just(callable()).
5115 3.34.0 - 2017-11-02
5117 Hypothesis now emits deprecation warnings if you apply @given more than
5118 once to a target.
5119 Applying @given repeatedly wraps the target multiple times. Each wrap‐
5121 per will search the space of of possible parameters separately. This
5122 is equivalent but will be much more inefficient than doing it with a
5123 single call to @given.
5124 For example, instead of @given(booleans()) @given(integers()), you
5126 could write @given(booleans(), integers())
5127 3.33.1 - 2017-11-02
5129 This is a bugfix release:
5130 · builds() would try to infer a strategy for required positional argu‐
5132 ments of the target from type hints, even if they had been given to
5133 builds() as positional arguments (issue #946). Now it only infers
5134 missing required arguments.
5135 · An internal introspection function wrongly reported self as a
5137 required argument for bound methods, which might also have affected
5138 builds(). Now it knows better.
5139 3.33.0 - 2017-10-16
5141 This release supports strategy inference for more field types in Django
5142 models() - you can now omit an argument for Date, Time, Duration, Slug,
5143 IP Address, and UUID fields. (issue #642)
5144 Strategy generation for fields with grouped choices now selects choices
5146 from each group, instead of selecting from the group names.
5147 3.32.2 - 2017-10-15
5149 This patch removes the mergedb tool, introduced in Hypothesis 1.7.1 on
5150 an experimental basis. It has never actually worked, and the new
5151 Hypothesis example database is designed to make such a tool unneces‐
5152 sary.
5153 3.32.1 - 2017-10-13
5155 This patch has two improvements for strategies based on enumerations.
5156 · from_type() now handles enumerations correctly, delegating to sam‐
5158 pled_from(). Previously it noted that Enum.__init__ has no required
5159 arguments and therefore delegated to builds(), which would subse‐
5160 quently fail.
5161 · When sampling from an python:enum.Flag, we also generate combinations
5163 of members. Eg for Flag('Permissions', 'READ, WRITE, EXECUTE') we can
5164 now generate, Permissions.READ, Permissions.READ|WRITE, and so on.
5165 3.32.0 - 2017-10-09
5167 This changes the default value of use_coverage=True to True when run‐
5168 ning on pypy (it was already True on CPython).
5169 It was previously set to False because we expected it to be too slow,
5171 but recent benchmarking shows that actually performance of the feature
5172 on pypy is fairly acceptable - sometimes it's slower than on CPython,
5173 sometimes it's faster, but it's generally within a factor of two either
5174 way.
5175 3.31.6 - 2017-10-08
5177 This patch improves the quality of strategies inferred from Numpy
5178 dtypes:
5179 · Integer dtypes generated examples with the upper half of their
5181 (non-sign) bits set to zero. The inferred strategies can now produce
5182 any representable integer.
5183 · Fixed-width unicode- and byte-string dtypes now cap the internal
5185 example length, which should improve example and shrink quality.
5186 · Numpy arrays can only store fixed-size strings internally, and allow
5188 shorter strings by right-padding them with null bytes. Inferred
5189 string strategies no longer generate such values, as they can never
5190 be retrieved from an array. This improves shrinking performance by
5191 skipping useless values.
5192 This has already been useful in Hypothesis - we found an overflow bug
5194 in our Pandas support, and as a result indexes() and range_indexes()
5195 now check that min_size and max_size are at least zero.
5196 3.31.5 - 2017-10-08
5198 This release fixes a performance problem in tests where use_coverage is
5199 set to True.
5200 Tests experience a slow-down proportionate to the amount of code they
5202 cover. This is still the case, but the factor is now low enough that
5203 it should be unnoticeable. Previously it was large and became much
5204 larger in 3.30.4.
5205 3.31.4 - 2017-10-08
5207 from_type() failed with a very confusing error if passed a NewType() (‐
5208 issue #901). These psudeo-types are now unwrapped correctly, and
5209 strategy inference works as expected.
5210 3.31.3 - 2017-10-06
5212 This release makes some small optimisations to our use of coverage that
5213 should reduce constant per-example overhead. This is probably only
5214 noticeable on examples where the test itself is quite fast. On no-op
5215 tests that don't test anything you may see up to a fourfold speed
5216 increase (which is still significantly slower than without coverage).
5217 On more realistic tests the speed up is likely to be less than that.
5218 3.31.2 - 2017-09-30
5220 This release fixes some formatting and small typos/grammar issues in
5221 the documentation, specifically the page docs/settings.rst, and the
5222 inline docs for the various settings.
5223 3.31.1 - 2017-09-30
5225 This release improves the handling of deadlines so that they act better
5226 with the shrinking process. This fixes issue #892.
5227 This involves two changes:
5229 1. The deadline is raised during the initial generation and shrinking,
5231 and then lowered to the set value for final replay. This restricts
5232 our attention to examples which exceed the deadline by a more sig‐
5233 nificant margin, which increases their reliability.
5234 2. When despite the above a test still becomes flaky because it is sig‐
5236 nificantly faster on rerun than it was on its first run, the error
5237 message is now more explicit about the nature of this problem, and
5238 includes both the initial test run time and the new test run time.
5239 In addition, this release also clarifies the documentation of the dead‐
5241 line setting slightly to be more explicit about where it applies.
5242 This work was funded by Smarkets.
5244 3.31.0 - 2017-09-29
5246 This release blocks installation of Hypothesis on Python 3.3, which
5247 reached its end of life date on 2017-09-29.
5248 This should not be of interest to anyone but downstream maintainers -
5250 if you are affected, migrate to a secure version of Python as soon as
5251 possible or at least seek commercial support.
5252 3.30.4 - 2017-09-27
5254 This release makes several changes:
5255 1. It significantly improves Hypothesis's ability to use coverage
5257 information to find interesting examples.
5258 2. It reduces the default max_examples setting from 200 to 100. This
5260 takes advantage of the improved algorithm meaning fewer examples are
5261 typically needed to get the same testing and is sufficiently better
5262 at covering interesting behaviour, and offsets some of the perfor‐
5263 mance problems of running under coverage.
5264 3. Hypothesis will always try to start its testing with an example that
5266 is near minimized.
5267 The new algorithm for 1 also makes some changes to Hypothesis's low
5269 level data generation which apply even with coverage turned off. They
5270 generally reduce the total amount of data generated, which should
5271 improve test performance somewhat. Between this and 3 you should see a
5272 noticeable reduction in test runtime (how much so depends on your tests
5273 and how much example size affects their performance. On our benchmarks,
5274 where data generation dominates, we saw up to a factor of two perfor‐
5275 mance improvement, but it's unlikely to be that large.
5276 3.30.3 - 2017-09-25
5278 This release fixes some formatting and small typos/grammar issues in
5279 the documentation, specifically the page docs/details.rst, and some
5280 inline docs linked from there.
5281 3.30.2 - 2017-09-24
5283 This release changes Hypothesis's caching approach for functions in
5284 hypothesis.strategies. Previously it would have cached extremely
5285 aggressively and cache entries would never be evicted. Now it adopts a
5286 least-frequently used, least recently used key invalidation policy, and
5287 is somewhat more conservative about which strategies it caches.
5288 Workloads which create strategies based on dynamic values, e.g. by
5290 using flatmap or composite(), will use significantly less memory.
5291 3.30.1 - 2017-09-22
5293 This release fixes a bug where when running with use_coverage=True
5294 inside an existing running instance of coverage, Hypothesis would fre‐
5295 quently put files that the coveragerc excluded in the report for the
5296 enclosing coverage.
5297 3.30.0 - 2017-09-20
5299 This release introduces two new features:
5300 · When a test fails, either with a health check failure or a falsifying
5302 example, Hypothesis will print out a seed that led to that failure,
5303 if the test is not already running with a fixed seed. You can then
5304 recreate that failure using either the @seed decorator or (if you are
5305 running pytest) with --hypothesis-seed.
5306 · pytest users can specify a seed to use for @given based tests by
5308 passing the --hypothesis-seed command line argument.
5309 This work was funded by Smarkets.
5311 3.29.0 - 2017-09-19
5313 This release makes Hypothesis coverage aware. Hypothesis now runs all
5314 test bodies under coverage, and uses this information to guide its
5315 testing.
5316 The use_coverage setting can be used to disable this behaviour if you
5318 want to test code that is sensitive to coverage being enabled (either
5319 because of performance or interaction with the trace function).
5320 The main benefits of this feature are:
5322 · Hypothesis now observes when examples it discovers cover particular
5324 lines or branches and stores them in the database for later.
5325 · Hypothesis will make some use of this information to guide its explo‐
5327 ration of the search space and improve the examples it finds (this is
5328 currently used only very lightly and will likely improve signifi‐
5329 cantly in future releases).
5330 This also has the following side-effects:
5332 · Hypothesis now has an install time dependency on the coverage pack‐
5334 age.
5335 · Tests that are already running Hypothesis under coverage will likely
5337 get faster.
5338 · Tests that are not running under coverage now run their test bodies
5340 under coverage by default.
5341 This feature is only partially supported under pypy. It is signifi‐
5343 cantly slower than on CPython and is turned off by default as a result,
5344 but it should still work correctly if you want to use it.
5345 3.28.3 - 2017-09-18
5347 This release is an internal change that affects how Hypothesis handles
5348 calculating certain properties of strategies.
5349 The primary effect of this is that it fixes a bug where use of
5351 deferred() could sometimes trigger an internal assertion error. However
5352 the fix for this bug involved some moderately deep changes to how
5353 Hypothesis handles certain constructs so you may notice some additional
5354 knock-on effects.
5355 In particular the way Hypothesis handles drawing data from strategies
5357 that cannot generate any values has changed to bail out sooner than it
5358 previously did. This may speed up certain tests, but it is unlikely to
5359 make much of a difference in practice for tests that were not already
5360 failing with Unsatisfiable.
5361 3.28.2 - 2017-09-18
5363 This is a patch release that fixes a bug in the hypothesis.extra.pandas
5364 documentation where it incorrectly referred to column() instead of col‐
5365 umns().
5366 3.28.1 - 2017-09-16
5368 This is a refactoring release. It moves a number of internal uses of
5369 namedtuple() over to using attrs based classes, and removes a couple of
5370 internal namedtuple classes that were no longer in use.
5371 It should have no user visible impact.
5373 3.28.0 - 2017-09-15
5375 This release adds support for testing pandas via the hypothe‐
5376 sis.extra.pandas module.
5377 It also adds a dependency on attrs.
5379 This work was funded by Stripe.
5381 3.27.1 - 2017-09-14
5383 This release fixes some formatting and broken cross-references in the
5384 documentation, which includes editing docstrings - and thus a patch
5385 release.
5386 3.27.0 - 2017-09-13
5388 This release introduces a deadline setting to Hypothesis.
5389 When set this turns slow tests into errors. By default it is unset but
5391 will warn if you exceed 200ms, which will become the default value in a
5392 future release.
5393 This work was funded by Smarkets.
5395 3.26.0 - 2017-09-12
5397 Hypothesis now emits deprecation warnings if you are using the legacy
5398 SQLite example database format, or the tool for merging them. These
5399 were already documented as deprecated, so this doesn't change their
5400 deprecation status, only that we warn about it.
5401 3.25.1 - 2017-09-12
5403 This release fixes a bug with generating numpy datetime and timedelta
5404 types: When inferring the strategy from the dtype, datetime and
5405 timedelta dtypes with sub-second precision would always produce exam‐
5406 ples with one second resolution. Inferring a strategy from a time
5407 dtype will now always produce example with the same precision.
5408 3.25.0 - 2017-09-12
5410 This release changes how Hypothesis shrinks and replays examples to
5411 take into account that it can encounter new bugs while shrinking the
5412 bug it originally found. Previously it would end up replacing the orig‐
5413 inally found bug with the new bug and show you only that one. Now it is
5414 (often) able to recognise when two bugs are distinct and when it finds
5415 more than one will show both.
5416 3.24.2 - 2017-09-11
5418 This release removes the (purely internal and no longer useful) strat‐
5419 egy_test_suite function and the corresponding strategytests module.
5420 3.24.1 - 2017-09-06
5422 This release improves the reduction of examples involving floating
5423 point numbers to produce more human readable examples.
5424 It also has some general purpose changes to the way the minimizer works
5426 internally, which may see some improvement in quality and slow down of
5427 test case reduction in cases that have nothing to do with floating
5428 point numbers.
5429 3.24.0 - 2017-09-05
5431 Hypothesis now emits deprecation warnings if you use some_strat‐
5432 egy.example() inside a test function or strategy definition (this was
5433 never intended to be supported, but is sufficiently widespread that it
5434 warrants a deprecation path).
5435 3.23.3 - 2017-09-05
5437 This is a bugfix release for decimals() with the places argument.
5438 · No longer fails health checks (issue #725, due to internal filtering)
5440 · Specifying a min_value and max_value without any decimals with places
5442 places between them gives a more useful error message.
5443 · Works for any valid arguments, regardless of the decimal precision
5445 context.
5446 3.23.2 - 2017-09-01
5448 This is a small refactoring release that removes a now-unused parameter
5449 to an internal API. It shouldn't have any user visible effect.
5450 3.23.1 - 2017-09-01
5452 Hypothesis no longer propagates the dynamic scope of settings into
5453 strategy definitions.
5454 This release is a small change to something that was never part of the
5456 public API and you will almost certainly not notice any effect unless
5457 you're doing something surprising, but for example the following code
5458 will now give a different answer in some circumstances:
5459 import hypothesis.strategies as st
5461 from hypothesis import settings
5462 CURRENT_SETTINGS = st.builds(lambda: settings.default)
5464 (We don't actually encourage you writing code like this)
5466 Previously this would have generated the settings that were in effect
5468 at the point of definition of CURRENT_SETTINGS. Now it will generate
5469 the settings that are used for the current test.
5470 It is very unlikely to be significant enough to be visible, but you may
5472 also notice a small performance improvement.
5473 3.23.0 - 2017-08-31
5475 This release adds a unique argument to arrays() which behaves the same
5476 ways as the corresponding one for lists(), requiring all of the ele‐
5477 ments in the generated array to be distinct.
5478 3.22.2 - 2017-08-29
5480 This release fixes an issue where Hypothesis would raise a TypeError
5481 when using the datetime-related strategies if running with PYTHONOPTI‐
5482 MIZE=2. This bug was introduced in 3.20.0. (See issue #822)
5483 3.22.1 - 2017-08-28
5485 Hypothesis now transparently handles problems with an internal unicode
5486 cache, including file truncation or read-only filesystems (issue #767).
5487 Thanks to Sam Hames for the patch.
5488 3.22.0 - 2017-08-26
5490 This release provides what should be a substantial performance improve‐
5491 ment to numpy arrays generated using provided numpy support, and adds a
5492 new fill_value argument to arrays() to control this behaviour.
5493 This work was funded by Stripe.
5495 3.21.3 - 2017-08-26
5497 This release fixes some extremely specific circumstances that probably
5498 have never occurred in the wild where users of deferred() might have
5499 seen a RuntimeError from too much recursion, usually in cases where no
5500 valid example could have been generated anyway.
5501 3.21.2 - 2017-08-25
5503 This release fixes some minor bugs in argument validation:
5504 · hypothesis.extra.numpy dtype strategies would raise an internal
5506 error instead of an InvalidArgument exception when passed an
5507 invalid endianness specification.
5508 · fractions() would raise an internal error instead of an InvalidAr‐
5510 gument if passed float("nan") as one of its bounds.
5511 · The error message for passing float("nan") as a bound to various
5513 strategies has been improved.
5514 · Various bound arguments will now raise InvalidArgument in cases
5516 where they would previously have raised an internal TypeError or
5517 ValueError from the relevant conversion function.
5518 · streaming() would not have emitted a deprecation warning when
5520 called with an invalid argument.
5521 3.21.1 - 2017-08-24
5523 This release fixes a bug where test failures that were the result of an
5524 @example would print an extra stack trace before re-raising the excep‐
5525 tion.
5526 3.21.0 - 2017-08-23
5528 This release deprecates Hypothesis's strict mode, which turned Hypothe‐
5529 sis's deprecation warnings into errors. Similar functionality can be
5530 achieved by using simplefilter('error', HypothesisDeprecationWarning).
5531 3.20.0 - 2017-08-22
5533 This release renames the relevant arguments on the datetimes(),
5534 dates(), times(), and timedeltas() strategies to min_value and
5535 max_value, to make them consistent with the other strategies in the
5536 module.
5537 The old argument names are still supported but will emit a deprecation
5539 warning when used explicitly as keyword arguments. Arguments passed
5540 positionally will go to the new argument names and are not deprecated.
5541 3.19.3 - 2017-08-22
5543 This release provides a major overhaul to the internals of how Hypothe‐
5544 sis handles shrinking.
5545 This should mostly be visible in terms of getting better examples for
5547 tests which make heavy use of composite(), data() or flatmap where the
5548 data drawn depends a lot on previous choices, especially where size
5549 parameters are affected. Previously Hypothesis would have struggled to
5550 reliably produce good examples here. Now it should do much better. Per‐
5551 formance should also be better for examples with a non-zero min_size.
5552 You may see slight changes to example generation (e.g. improved example
5554 diversity) as a result of related changes to internals, but they are
5555 unlikely to be significant enough to notice.
5556 3.19.2 - 2017-08-21
5558 This release fixes two bugs in hypothesis.extra.numpy:
5559 · unicode_string_dtypes() didn't work at all due to an incorrect dtype
5561 specifier. Now it does.
5562 · Various impossible conditions would have been accepted but would
5564 error when they fail to produced any example. Now they raise an
5565 explicit InvalidArgument error.
5566 3.19.1 - 2017-08-21
5568 This is a bugfix release for issue #739, where bounds for fractions()
5569 or floating-point decimals() were not properly converted to integers
5570 before passing them to the integers strategy. This excluded some val‐
5571 ues that should have been possible, and could trigger internal errors
5572 if the bounds lay between adjacent integers.
5573 You can now bound fractions() with two arbitrarily close fractions.
5575 It is now an explicit error to supply a min_value, max_value, and
5577 max_denominator to fractions() where the value bounds do not include a
5578 fraction with denominator at most max_denominator.
5579 3.19.0 - 2017-08-20
5581 This release adds the from_regex() strategy, which generates strings
5582 that contain a match of a regular expression.
5583 Thanks to Maxim Kulkin for creating the hypothesis-regex package and
5585 then helping to upstream it! (issue #662)
5586 3.18.5 - 2017-08-18
5588 This is a bugfix release for integers(). Previously the strategy would
5589 hit an internal assertion if passed non-integer bounds for min_value
5590 and max_value that had no integers between them. The strategy now
5591 raises InvalidArgument instead.
5592 3.18.4 - 2017-08-18
5594 Release to fix a bug where mocks can be used as test runners under cer‐
5595 tain conditions. Specifically, if a mock is injected into a test via
5596 pytest fixtures or patch decorators, and that mock is the first argu‐
5597 ment in the list, hypothesis will think it represents self and turns
5598 the mock into a test runner. If this happens, the affected test always
5599 passes because the mock is executed instead of the test body. Some‐
5600 times, it will also fail health checks.
5601 Fixes issue #491 and a section of issue #198. Thanks to Ben Peterson
5603 for this bug fix.
5604 3.18.3 - 2017-08-17
5606 This release should improve the performance of some tests which experi‐
5607 enced a slow down as a result of the 3.13.0 release.
5608 Tests most likely to benefit from this are ones that make extensive use
5610 of min_size parameters, but others may see some improvement as well.
5611 3.18.2 - 2017-08-16
5613 This release fixes a bug introduced in 3.18.0. If the arguments
5614 whitelist_characters and blacklist_characters to characters() both con‐
5615 tained elements, then an InvalidArgument exception would be raised.
5616 Thanks to Zac Hatfield-Dodds for reporting and fixing this.
5618 3.18.1 - 2017-08-14
5620 This is a bug fix release to fix issue #780, where sets() and similar
5621 would trigger health check errors if their element strategy could only
5622 produce one element (e.g. if it was just()).
5623 3.18.0 - 2017-08-13
5625 This is a feature release:
5626 · characters() now accepts whitelist_characters, particular characters
5628 which will be added to those it produces. (issue #668)
5629 · A bug fix for the internal function _union_interval_lists(), and a
5631 rename to _union_intervals(). It now correctly handles all cases
5632 where intervals overlap, and it always returns the result as a tuple
5633 for tuples.
5634 Thanks to Alex Willmer for these.
5636 3.17.0 - 2017-08-07
5638 This release documents the previously undocumented phases feature, mak‐
5639 ing it part of the public API. It also updates how the example database
5640 is used. Principally:
5641 · A Phases.reuse argument will now correctly control whether examples
5643 from the database are run (it previously did exactly the wrong thing
5644 and controlled whether examples would be saved).
5645 · Hypothesis will no longer try to rerun all previously failing exam‐
5647 ples. Instead it will replay the smallest previously failing example
5648 and a selection of other examples that are likely to trigger any
5649 other bugs that will found. This prevents a previous failure from
5650 dominating your tests unnecessarily.
5651 · As a result of the previous change, Hypothesis will be slower about
5653 clearing out old examples from the database that are no longer fail‐
5654 ing (because it can only clear out ones that it actually runs).
5655 3.16.1 - 2017-08-07
5657 This release makes an implementation change to how Hypothesis handles
5658 certain internal constructs.
5659 The main effect you should see is improvement to the behaviour and per‐
5661 formance of collection types, especially ones with a min_size parame‐
5662 ter. Many cases that would previously fail due to being unable to gen‐
5663 erate enough valid examples will now succeed, and other cases should
5664 run slightly faster.
5665 3.16.0 - 2017-08-04
5667 This release introduces a deprecation of the timeout feature. This
5668 results in the following changes:
5669 · Creating a settings object with an explicit timeout will emit a dep‐
5671 recation warning.
5672 · If your test stops because it hits the timeout (and has not found a
5674 bug) then it will emit a deprecation warning.
5675 · There is a new value unlimited which you can import from hypothesis.
5677 settings(timeout=unlimited) will not cause a deprecation warning.
5678 · There is a new health check, hung_test, which will trigger after a
5680 test has been running for five minutes if it is not suppressed.
5681 3.15.0 - 2017-08-04
5683 This release deprecates two strategies, choices() and streaming().
5684 Both of these are somewhat confusing to use and are entirely redundant
5686 since the introduction of the data() strategy for interactive drawing
5687 in tests, and their use should be replaced with direct use of data()
5688 instead.
5689 3.14.2 - 2017-08-03
5691 This fixes a bug where Hypothesis would not work correctly on Python
5692 2.7 if you had the python:typing module backport installed.
5693 3.14.1 - 2017-08-02
5695 This raises the maximum depth at which Hypothesis starts cutting off
5696 data generation to a more reasonable value which it is harder to hit by
5697 accident.
5698 This resolves (issue #751), in which some examples which previously
5700 worked would start timing out, but it will also likely improve the data
5701 generation quality for complex data types.
5702 3.14.0 - 2017-07-23
5704 Hypothesis now understands inline type annotations (issue #293):
5705 · If the target of builds() has type annotations, a default strategy
5707 for missing required arguments is selected based on the type.
5708 Type-based strategy selection will only override a default if you
5709 pass hypothesis.infer as a keyword argument.
5710 · If @given wraps a function with type annotations, you can pass infer
5712 as a keyword argument and the appropriate strategy will be substi‐
5713 tuted.
5714 · You can check what strategy will be inferred for a type with the new
5716 from_type() function.
5717 · register_type_strategy() teaches Hypothesis which strategy to infer
5719 for custom or unknown types. You can provide a strategy, or for more
5720 complex cases a function which takes the type and returns a strategy.
5721 3.13.1 - 2017-07-20
5723 This is a bug fix release for issue #514 - Hypothesis would continue
5724 running examples after a SkipTest exception was raised, including
5725 printing a falsifying example. Skip exceptions from the standard
5726 python:unittest module, and pytest, nose, or unittest2 modules now
5727 abort the test immediately without printing output.
5728 3.13.0 - 2017-07-16
5730 This release has two major aspects to it: The first is the introduction
5731 of deferred(), which allows more natural definition of recursive
5732 (including mutually recursive) strategies.
5733 The second is a number of engine changes designed to support this sort
5735 of strategy better. These should have a knock-on effect of also improv‐
5736 ing the performance of any existing strategies that currently generate
5737 a lot of data or involve heavy nesting by reducing their typical exam‐
5738 ple size.
5739 3.12.0 - 2017-07-07
5741 This release makes some major internal changes to how Hypothesis repre‐
5742 sents data internally, as a prelude to some major engine changes that
5743 should improve data quality. There are no API changes, but it's a sig‐
5744 nificant enough internal change that a minor version bump seemed war‐
5745 ranted.
5746 User facing impact should be fairly mild, but includes:
5748 · All existing examples in the database will probably be invalidated.
5750 Hypothesis handles this automatically, so you don't need to do any‐
5751 thing, but if you see all your examples disappear that's why.
5752 · Almost all data distributions have changed significantly. Possibly
5754 for the better, possibly for the worse. This may result in new bugs
5755 being found, but it may also result in Hypothesis being unable to
5756 find bugs it previously did.
5757 · Data generation may be somewhat faster if your existing bottleneck
5759 was in draw_bytes (which is often the case for large examples).
5760 · Shrinking will probably be slower, possibly significantly.
5762 If you notice any effects you consider to be a significant regression,
5764 please open an issue about them.
5765 3.11.6 - 2017-06-19
5767 This release involves no functionality changes, but is the first to
5768 ship wheels as well as an sdist.
5769 3.11.5 - 2017-06-18
5771 This release provides a performance improvement to shrinking. For cases
5772 where there is some non-trivial "boundary" value (e.g. the bug happens
5773 for all values greater than some other value), shrinking should now be
5774 substantially faster. Other types of bug will likely see improvements
5775 too.
5776 This may also result in some changes to the quality of the final exam‐
5778 ples - it may sometimes be better, but is more likely to get slightly
5779 worse in some edge cases. If you see any examples where this happens in
5780 practice, please report them.
5781 3.11.4 - 2017-06-17
5783 This is a bugfix release: Hypothesis now prints explicit examples when
5784 running in verbose mode. (issue #313)
5785 3.11.3 - 2017-06-11
5787 This is a bugfix release: Hypothesis no longer emits a warning if you
5788 try to use sampled_from() with python:collections.OrderedDict. (issue
5789 #688)
5790 3.11.2 - 2017-06-10
5792 This is a documentation release. Several outdated snippets have been
5793 updated or removed, and many cross-references are now hyperlinks.
5794 3.11.1 - 2017-05-28
5796 This is a minor ergonomics release. Tracebacks shown by pytest no
5797 longer include Hypothesis internals for test functions decorated with
5798 @given.
5799 3.11.0 - 2017-05-23
5801 This is a feature release, adding datetime-related strategies to the
5802 core strategies.
5803 timezones() allows you to sample pytz timezones from the Olsen data‐
5805 base. Use directly in a recipe for tz-aware datetimes, or compose with
5806 none() to allow a mix of aware and naive output.
5807 The new dates(), times(), datetimes(), and timedeltas() strategies are
5809 all constrained by objects of their type. This means that you can gen‐
5810 erate dates bounded by a single day (i.e. a single date), or datetimes
5811 constrained to the microsecond.
5812 times() and datetimes() take an optional timezones= argument, which
5814 defaults to none() for naive times. You can use our extra strategy
5815 based on pytz, or roll your own timezones strategy with dateutil or
5816 even the standard library.
5817 The old dates, times, and datetimes strategies in hypothe‐
5819 sis.extra.datetimes are deprecated in favor of the new core strategies,
5820 which are more flexible and have no dependencies.
5821 3.10.0 - 2017-05-22
5823 Hypothesis now uses python:inspect.getfullargspec() internally. On
5824 Python 2, there are no visible changes.
5825 On Python 3 @given and @composite now preserve PEP 3107 annotations on
5827 the decorated function. Keyword-only arguments are now either handled
5828 correctly (e.g. @composite), or caught in validation instead of
5829 silently discarded or raising an unrelated error later (e.g. @given).
5830 3.9.1 - 2017-05-22
5832 This is a bugfix release: the default field mapping for a DateTimeField
5833 in the Django extra now respects the USE_TZ setting when choosing a
5834 strategy.
5835 3.9.0 - 2017-05-19
5837 This is feature release, expanding the capabilities of the decimals()
5838 strategy.
5839 · The new (optional) places argument allows you to generate decimals
5841 with a certain number of places (e.g. cents, thousandths, satoshis).
5842 · If allow_infinity is None, setting min_bound no longer excludes posi‐
5844 tive infinity and setting max_value no longer excludes negative
5845 infinity.
5846 · All of NaN, -Nan, sNaN, and -sNaN may now be drawn if allow_nan is
5848 True, or if allow_nan is None and min_value or max_value is None.
5849 · min_value and max_value may be given as decimal strings, e.g.
5851 "1.234".
5852 3.8.5 - 2017-05-16
5854 Hypothesis now imports python:sqlite3 when a SQLite database is used,
5855 rather than at module load, improving compatibility with Python imple‐
5856 mentations compiled without SQLite support (such as BSD or Jython).
5857 3.8.4 - 2017-05-16
5859 This is a compatibility bugfix release. sampled_from no longer raises
5860 a deprecation warning when sampling from an Enum, as all enums have a
5861 reliable iteration order.
5862 3.8.3 - 2017-05-09
5864 This release removes a version check for older versions of pytest when
5865 using the Hypothesis pytest plugin. The pytest plugin will now run
5866 unconditionally on all versions of pytest. This breaks compatibility
5867 with any version of pytest prior to 2.7.0 (which is more than two years
5868 old).
5869 The primary reason for this change is that the version check was a fre‐
5871 quent source of breakage when pytest change their versioning scheme. If
5872 you are not working on pytest itself and are not running a very old
5873 version of it, this release probably doesn't affect you.
5874 3.8.2 - 2017-04-26
5876 This is a code reorganisation release that moves some internal test
5877 helpers out of the main source tree so as to not have changes to them
5878 trigger releases in future.
5879 3.8.1 - 2017-04-26
5881 This is a documentation release. Almost all code examples are now
5882 doctests checked in CI, eliminating stale examples.
5883 3.8.0 - 2017-04-23
5885 This is a feature release, adding the iterables() strategy, equivalent
5886 to lists(...).map(iter) but with a much more useful repr. You can use
5887 this strategy to check that code doesn't accidentally depend on
5888 sequence properties such as indexing support or repeated iteration.
5889 3.7.4 - 2017-04-22
5891 This patch fixes a bug in 3.7.3, where using @example and a pytest fix‐
5892 ture in the same test could cause the test to fail to fill the argu‐
5893 ments, and throw a TypeError.
5894 3.7.3 - 2017-04-21
5896 This release should include no user visible changes and is purely a
5897 refactoring release. This modularises the behaviour of the core given()
5898 function, breaking it up into smaller and more accessible parts, but
5899 its actual behaviour should remain unchanged.
5900 3.7.2 - 2017-04-21
5902 This reverts an undocumented change in 3.7.1 which broke installation
5903 on debian stable: The specifier for the hypothesis[django]
5904 extra_requires had introduced a wild card, which was not supported on
5905 the default version of pip.
5906 3.7.1 - 2017-04-21
5908 This is a bug fix and internal improvements release.
5909 · In particular Hypothesis now tracks a tree of where it has already
5911 explored. This allows it to avoid some classes of duplicate exam‐
5912 ples, and significantly improves the performance of shrinking failing
5913 examples by allowing it to skip some shrinks that it can determine
5914 can't possibly work.
5915 · Hypothesis will no longer seed the global random arbitrarily unless
5917 you have asked it to using random_module()
5918 · Shrinking would previously have not worked correctly in some special
5920 cases on Python 2, and would have resulted in suboptimal examples.
5921 3.7.0 - 2017-03-20
5923 This is a feature release.
5924 New features:
5926 · Rule based stateful testing now has an @invariant decorator that
5928 specifies methods that are run after init and after every step,
5929 allowing you to encode properties that should be true at all times.
5930 Thanks to Tom Prince for this feature.
5931 · The decimals() strategy now supports allow_nan and allow_infinity
5933 flags.
5934 · There are significantly more strategies available for numpy, includ‐
5936 ing for generating arbitrary data types. Thanks to Zac Hatfield Dodds
5937 for this feature.
5938 · When using the data() strategy you can now add a label as an argument
5940 to draw(), which will be printed along with the value when an example
5941 fails. Thanks to Peter Inglesby for this feature.
5942 Bug fixes:
5944 · Bug fix: composite() now preserves functions' docstrings.
5946 · The build is now reproducible and doesn't depend on the path you
5948 build it from. Thanks to Chris Lamb for this feature.
5949 · numpy strategies for the void data type did not work correctly.
5951 Thanks to Zac Hatfield Dodds for this fix.
5952 There have also been a number of performance optimizations:
5954 · The permutations() strategy is now significantly faster to use for
5956 large lists (the underlying algorithm has gone from O(n^2) to O(n)).
5957 · Shrinking of failing test cases should have got significantly faster
5959 in some circumstances where it was previously struggling for a long
5960 time.
5961 · Example generation now involves less indirection, which results in a
5963 small speedup in some cases (small enough that you won't really
5964 notice it except in pathological cases).
5965 3.6.1 - 2016-12-20
5967 This release fixes a dependency problem and makes some small behind the
5968 scenes improvements.
5969 · The fake-factory dependency was renamed to faker. If you were depend‐
5971 ing on it through hypothesis[django] or hypothesis[fake-factory]
5972 without pinning it yourself then it would have failed to install
5973 properly. This release changes it so that hypothesis[fakefactory]
5974 (which can now also be installed as hypothesis[faker]) will install
5975 the renamed faker package instead.
5976 · This release also removed the dependency of hypothesis[django] on
5978 hypothesis[fakefactory] - it was only being used for emails. These
5979 now use a custom strategy that isn't from fakefactory. As a result
5980 you should also see performance improvements of tests which generated
5981 User objects or other things with email fields, as well as better
5982 shrinking of email addresses.
5983 · The distribution of code using nested calls to one_of() or the |
5985 operator for combining strategies has been improved, as branches are
5986 now flattened to give a more uniform distribution.
5987 · Examples using composite() or .flatmap should now shrink better. In
5989 particular this will affect things which work by first generating a
5990 length and then generating that many items, which have historically
5991 not shrunk very well.
5992 3.6.0 - 2016-10-31
5994 This release reverts Hypothesis to its old pretty printing of lambda
5995 functions based on attempting to extract the source code rather than
5996 decompile the bytecode. This is unfortunately slightly inferior in
5997 some cases and may result in you occasionally seeing things like lambda
5998 x: <unknown> in statistics reports and strategy reprs.
5999 This removes the dependencies on uncompyle6, xdis and spark-parser.
6001 The reason for this is that the new functionality was based on
6003 uncompyle6, which turns out to introduce a hidden GPLed dependency - it
6004 in turn depended on xdis, and although the library was licensed under
6005 the MIT license, it contained some GPL licensed source code and thus
6006 should have been released under the GPL.
6007 My interpretation is that Hypothesis itself was never in violation of
6009 the GPL (because the license it is under, the Mozilla Public License
6010 v2, is fully compatible with being included in a GPL licensed work),
6011 but I have not consulted a lawyer on the subject. Regardless of the
6012 answer to this question, adding a GPLed dependency will likely cause a
6013 lot of users of Hypothesis to inadvertently be in violation of the GPL.
6014 As a result, if you are running Hypothesis 3.5.x you really should
6016 upgrade to this release immediately.
6017 3.5.3 - 2016-10-05
6019 This is a bug fix release.
6020 Bugs fixed:
6022 · If the same test was running concurrently in two processes and there
6024 were examples already in the test database which no longer failed,
6025 Hypothesis would sometimes fail with a FileNotFoundError (IOError on
6026 Python 2) because an example it was trying to read was deleted before
6027 it was read. (issue #372).
6028 · Drawing from an integers() strategy with both a min_value and a
6030 max_value would reject too many examples needlessly. Now it repeat‐
6031 edly redraws until satisfied. (pull request #366. Thanks to Calen
6032 Pennington for the contribution).
6033 3.5.2 - 2016-09-24
6035 This is a bug fix release.
6036 · The Hypothesis pytest plugin broke pytest support for doctests. Now
6038 it doesn't.
6039 3.5.1 - 2016-09-23
6041 This is a bug fix release.
6042 · Hypothesis now runs cleanly in -B and -BB modes, avoiding mixing
6044 bytes and unicode.
6045 · python:unittest.TestCase tests would not have shown up in the new
6047 statistics mode. Now they do.
6048 · Similarly, stateful tests would not have shown up in statistics and
6050 now they do.
6051 · Statistics now print with pytest node IDs (the names you'd get in
6053 pytest verbose mode).
6054 3.5.0 - 2016-09-22
6056 This is a feature release.
6057 · fractions() and decimals() strategies now support min_value and
6059 max_value parameters. Thanks go to Anne Mulhern for the development
6060 of this feature.
6061 · The Hypothesis pytest plugin now supports a --hypothesis-show-statis‐
6063 tics parameter that gives detailed statistics about the tests that
6064 were run. Huge thanks to Jean-Louis Fuchs and Adfinis-SyGroup for
6065 funding the development of this feature.
6066 · There is a new event() function that can be used to add custom sta‐
6068 tistics.
6069 Additionally there have been some minor bug fixes:
6071 · In some cases Hypothesis should produce fewer duplicate examples
6073 (this will mostly only affect cases with a single parameter).
6074 · py.test command line parameters are now under an option group for
6076 Hypothesis (thanks to David Keijser for fixing this)
6077 · Hypothesis would previously error if you used PEP 3107 function anno‐
6079 tations on your tests under Python 3.4.
6080 · The repr of many strategies using lambdas has been improved to
6082 include the lambda body (this was previously supported in many but
6083 not all cases).
6084 3.4.2 - 2016-07-13
6086 This is a bug fix release, fixing a number of problems with the set‐
6087 tings system:
6088 · Test functions defined using @given can now be called from other
6090 threads (issue #337)
6091 · Attempting to delete a settings property would previously have
6093 silently done the wrong thing. Now it raises an AttributeError.
6094 · Creating a settings object with a custom database_file parameter was
6096 silently getting ignored and the default was being used instead. Now
6097 it's not.
6098 3.4.1 - 2016-07-07
6100 This is a bug fix release for a single bug:
6101 · On Windows when running two Hypothesis processes in parallel (e.g.
6103 using pytest-xdist) they could race with each other and one would
6104 raise an exception due to the non-atomic nature of file renaming on
6105 Windows and the fact that you can't rename over an existing file.
6106 This is now fixed.
6107 3.4.0 - 2016-05-27
6109 This release is entirely provided by Lucas Wiman:
6110 Strategies constructed by models() will now respect much more of
6112 Django's validations out of the box. Wherever possible full_clean()
6113 should succeed.
6114 In particular:
6116 · The max_length, blank and choices kwargs are now respected.
6118 · Add support for DecimalField.
6120 · If a field includes validators, the list of validators are used to
6122 filter the field strategy.
6123 3.3.0 - 2016-05-27
6125 This release went wrong and is functionally equivalent to 3.2.0. Ignore
6126 it.
6127 3.2.0 - 2016-05-19
6129 This is a small single-feature release:
6130 · All tests using @given now fix the global random seed. This removes
6132 the health check for that. If a non-zero seed is required for the
6133 final falsifying example, it will be reported. Otherwise Hypothesis
6134 will assume randomization was not a significant factor for the test
6135 and be silent on the subject. If you use random_module() this will
6136 continue to work and will always display the seed.
6137 3.1.3 - 2016-05-01
6139 Single bug fix release
6140 · Another charmap problem. In 3.1.2 text() and characters() would break
6142 on systems which had /tmp mounted on a different partition than the
6143 Hypothesis storage directory (usually in home). This fixes that.
6144 3.1.2 - 2016-04-30
6146 Single bug fix release:
6147 · Anything which used a text() or characters() strategy was broken on
6149 Windows and I hadn't updated appveyor to use the new repository loca‐
6150 tion so I didn't notice. This is now fixed and windows support should
6151 work correctly.
6152 3.1.1 - 2016-04-29
6154 Minor bug fix release.
6155 · Fix concurrency issue when running tests that use text() from multi‐
6157 ple processes at once (issue #302, thanks to Alex Chan).
6158 · Improve performance of code using lists() with max_size (thanks to
6160 Cristi Cobzarenco).
6161 · Fix install on Python 2 with ancient versions of pip so that it
6163 installs the enum34 backport (thanks to Donald Stufft for telling me
6164 how to do this).
6165 · Remove duplicated __all__ exports from hypothesis.strategies (thanks
6167 to Piët Delport).
6168 · Update headers to point to new repository location.
6170 · Allow use of strategies that can't be used in find() (e.g. choices())
6172 in stateful testing.
6173 3.1.0 - 2016-03-06
6175 · Add a nothing() strategy that never successfully generates values.
6176 · sampled_from() and one_of() can both now be called with an empty
6178 argument list, in which case they also never generate any values.
6179 · one_of() may now be called with a single argument that is a collec‐
6181 tion of strategies as well as as varargs.
6182 · Add a runner() strategy which returns the instance of the current
6184 test object if there is one.
6185 · 'Bundle' for RuleBasedStateMachine is now a normal(ish) strategy and
6187 can be used as such.
6188 · Tests using RuleBasedStateMachine should now shrink significantly
6190 better.
6191 · Hypothesis now uses a pretty-printing library internally, compatible
6193 with IPython's pretty printing protocol (actually using the same
6194 code). This may improve the quality of output in some cases.
6195 · Add a 'phases' setting that allows more fine grained control over
6197 which parts of the process Hypothesis runs
6198 · Add a suppress_health_check setting which allows you to turn off spe‐
6200 cific health checks in a fine grained manner.
6201 · Fix a bug where lists of non fixed size would always draw one more
6203 element than they included. This mostly didn't matter, but if would
6204 cause problems with empty strategies or ones with side effects.
6205 · Add a mechanism to the Django model generator to allow you to explic‐
6207 itly request the default value (thanks to Jeremy Thurgood for this
6208 one).
6209 3.0.5 - 2016-02-25
6211 · Fix a bug where Hypothesis would now error on py.test development
6212 versions.
6213 3.0.4 - 2016-02-24
6215 · Fix a bug where Hypothesis would error when running on Python 2.7.3
6216 or earlier because it was trying to pass a python:bytearray object to
6217 python:struct.unpack() (which is only supported since 2.7.4).
6218 3.0.3 - 2016-02-23
6220 · Fix version parsing of py.test to work with py.test release candi‐
6221 dates
6222 · More general handling of the health check problem where things could
6224 fail because of a cache miss - now one "free" example is generated
6225 before the start of the health check run.
6226 3.0.2 - 2016-02-18
6228 · Under certain circumstances, strategies involving text() buried
6229 inside some other strategy (e.g. text().filter(...) or recur‐
6230 sive(text(), ...)) would cause a test to fail its health checks the
6231 first time it ran. This was caused by having to compute some related
6232 data and cache it to disk. On travis or anywhere else where the
6233 .hypothesis directory was recreated this would have caused the tests
6234 to fail their health check on every run. This is now fixed for all
6235 the known cases, although there could be others lurking.
6236 3.0.1 - 2016-02-18
6238 · Fix a case where it was possible to trigger an "Unreachable" asser‐
6239 tion when running certain flaky stateful tests.
6240 · Improve shrinking of large stateful tests by eliminating a case where
6242 it was hard to delete early steps.
6243 · Improve efficiency of drawing binary(min_size=n, max_size=n) signifi‐
6245 cantly by provide a custom implementation for fixed size blocks that
6246 can bypass a lot of machinery.
6247 · Set default home directory based on the current working directory at
6249 the point Hypothesis is imported, not whenever the function first
6250 happens to be called.
6251 3.0.0 - 2016-02-17
6253 Codename: This really should have been 2.1.
6254 Externally this looks like a very small release. It has one small
6256 breaking change that probably doesn't affect anyone at all (some behav‐
6257 iour that never really worked correctly is now outright forbidden) but
6258 necessitated a major version bump and one visible new feature.
6259 Internally this is a complete rewrite. Almost nothing other than the
6261 public API is the same.
6262 New features:
6264 · Addition of data() strategy which allows you to draw arbitrary data
6266 interactively within the test.
6267 · New "exploded" database format which allows you to more easily check
6269 the example database into a source repository while supporting merg‐
6270 ing.
6271 · Better management of how examples are saved in the database.
6273 · Health checks will now raise as errors when they fail. It was too
6275 easy to have the warnings be swallowed entirely.
6276 New limitations:
6278 · choices() and streaming() strategies may no longer be used with
6280 find(). Neither may data() (this is the change that necessitated a
6281 major version bump).
6282 Feature removal:
6284 · The ForkingTestCase executor has gone away. It may return in some
6286 more working form at a later date.
6287 Performance improvements:
6289 · A new model which allows flatmap, composite strategies and stateful
6291 testing to perform much better. They should also be more reliable.
6292 · Filtering may in some circumstances have improved significantly. This
6294 will help especially in cases where you have lots of values with
6295 individual filters on them, such as lists(x.filter(...)).
6296 · Modest performance improvements to the general test runner by avoid‐
6298 ing expensive operations
6299 In general your tests should have got faster. If they've instead got
6301 significantly slower, I'm interested in hearing about it.
6302 Data distribution:
6304 The data distribution should have changed significantly. This may
6306 uncover bugs the previous version missed. It may also miss bugs the
6307 previous version could have uncovered. Hypothesis is now producing less
6308 strongly correlated data than it used to, but the correlations are
6309 extended over more of the structure.
6310 Shrinking:
6312 Shrinking quality should have improved. In particular Hypothesis can
6314 now perform simultaneous shrinking of separate examples within a single
6315 test (previously it was only able to do this for elements of a single
6316 collection). In some cases performance will have improved, in some
6317 cases it will have got worse but generally shouldn't have by much.
6318 2.0.0 - 2016-01-10
6320 Codename: A new beginning
6321 This release cleans up all of the legacy that accrued in the course of
6323 Hypothesis 1.0. These are mostly things that were emitting deprecation
6324 warnings in 1.19.0, but there were a few additional changes.
6325 In particular:
6327 · non-strategy values will no longer be converted to strategies when
6329 used in given or find.
6330 · FailedHealthCheck is now an error and not a warning.
6332 · Handling of non-ascii reprs in user types have been simplified by
6334 using raw strings in more places in Python 2.
6335 · given no longer allows mixing positional and keyword arguments.
6337 · given no longer works with functions with defaults.
6339 · given no longer turns provided arguments into defaults - they will
6341 not appear in the argspec at all.
6342 · the basic() strategy no longer exists.
6344 · the n_ary_tree strategy no longer exists.
6346 · the average_list_length setting no longer exists. Note: If you're
6348 using using recursive() this will cause you a significant slow down.
6349 You should pass explicit average_size parameters to collections in
6350 recursive calls.
6351 · @rule can no longer be applied to the same method twice.
6353 · Python 2.6 and 3.3 are no longer officially supported, although in
6355 practice they still work fine.
6356 This also includes two non-deprecation changes:
6358 · given's keyword arguments no longer have to be the rightmost argu‐
6360 ments and can appear anywhere in the method signature.
6361 · The max_shrinks setting would sometimes not have been respected.
6363 1.19.0 - 2016-01-09
6365 Codename: IT COMES
6366 This release heralds the beginning of a new and terrible age of Hypoth‐
6368 esis 2.0.
6369 It's primary purpose is some final deprecations prior to said release.
6371 The goal is that if your code emits no warnings under this release then
6372 it will probably run unchanged under Hypothesis 2.0 (there are some
6373 caveats to this: 2.0 will drop support for some Python versions, and if
6374 you're using internal APIs then as usual that may break without warn‐
6375 ing).
6376 It does have two new features:
6378 · New @seed() decorator which allows you to manually seed a test. This
6380 may be harmlessly combined with and overrides the derandomize set‐
6381 ting.
6382 · settings objects may now be used as a decorator to fix those settings
6384 to a particular @given test.
6385 API changes (old usage still works but is deprecated):
6387 · Settings has been renamed to settings (lower casing) in order to make
6389 the decorator usage more natural.
6390 · Functions for the storage directory that were in hypothesis.settings
6392 are now in a new hypothesis.configuration module.
6393 Additional deprecations:
6395 · the average_list_length setting has been deprecated in favour of
6397 being explicit.
6398 · the basic() strategy has been deprecated as it is impossible to sup‐
6400 port it under a Conjecture based model, which will hopefully be
6401 implemented at some point in the 2.x series.
6402 · the n_ary_tree strategy (which was never actually part of the public
6404 API) has been deprecated.
6405 · Passing settings or random as keyword arguments to given is depre‐
6407 cated (use the new functionality instead)
6408 Bug fixes:
6410 · No longer emit PendingDeprecationWarning for __iter__ and StopItera‐
6412 tion in streaming() values.
6413 · When running in health check mode with non strict, don't print quite
6415 so many errors for an exception in reify.
6416 · When an assumption made in a test or a filter is flaky, tests will
6418 now raise Flaky instead of UnsatisfiedAssumption.
6419 1.18.1 - 2015-12-22
6421 Two behind the scenes changes:
6422 · Hypothesis will no longer write generated code to the file system.
6424 This will improve performance on some systems (e.g. if you're using
6425 PythonAnywhere which is running your code from NFS) and prevent some
6426 annoying interactions with auto-restarting systems.
6427 · Hypothesis will cache the creation of some strategies. This can sig‐
6429 nificantly improve performance for code that uses flatmap or compos‐
6430 ite and thus has to instantiate strategies a lot.
6431 1.18.0 - 2015-12-21
6433 Features:
6434 · Tests and find are now explicitly seeded off the global random mod‐
6436 ule. This means that if you nest one inside the other you will now
6437 get a health check error. It also means that you can control global
6438 randomization by seeding random.
6439 · There is a new random_module() strategy which seeds the global random
6441 module for you and handles things so that you don't get a health
6442 check warning if you use it inside your tests.
6443 · floats() now accepts two new arguments: allow_nan and allow_infinity.
6445 These default to the old behaviour, but when set to False will do
6446 what the names suggest.
6447 Bug fixes:
6449 · Fix a bug where tests that used text() on Python 3.4+ would not actu‐
6451 ally be deterministic even when explicitly seeded or using the deran‐
6452 domize mode, because generation depended on dictionary iteration
6453 order which was affected by hash randomization.
6454 · Fix a bug where with complicated strategies the timing of the initial
6456 health check could affect the seeding of the subsequent test, which
6457 would also render supposedly deterministic tests non-deterministic in
6458 some scenarios.
6459 · In some circumstances flatmap() could get confused by two struc‐
6461 turally similar things it could generate and would produce a flaky
6462 test where the first time it produced an error but the second time it
6463 produced the other value, which was not an error. The same bug was
6464 presumably also possible in composite().
6465 · flatmap() and composite() initial generation should now be moderately
6467 faster. This will be particularly noticeable when you have many val‐
6468 ues drawn from the same strategy in a single run, e.g. constructs
6469 like lists(s.flatmap(f)). Shrinking performance may have suffered,
6470 but this didn't actually produce an interestingly worse result in any
6471 of the standard scenarios tested.
6472 1.17.1 - 2015-12-16
6474 A small bug fix release, which fixes the fact that the 'note' function
6475 could not be used on tests which used the @example decorator to provide
6476 explicit examples.
6477 1.17.0 - 2015-12-15
6479 This is actually the same release as 1.16.1, but 1.16.1 has been pulled
6480 because it contains the following additional change that was not
6481 intended to be in a patch release (it's perfectly stable, but is a
6482 larger change that should have required a minor version bump):
6483 · Hypothesis will now perform a series of "health checks" as part of
6485 running your tests. These detect and warn about some common error
6486 conditions that people often run into which wouldn't necessarily have
6487 caused the test to fail but would cause e.g. degraded performance or
6488 confusing results.
6489 1.16.1 - 2015-12-14
6491 Note: This release has been removed.
6492 A small bugfix release that allows bdists for Hypothesis to be built
6494 under 2.7 - the compat3.py file which had Python 3 syntax wasn't
6495 intended to be loaded under Python 2, but when building a bdist it was.
6496 In particular this would break running setup.py test.
6497 1.16.0 - 2015-12-08
6499 There are no public API changes in this release but it includes a be‐
6500 haviour change that I wasn't comfortable putting in a patch release.
6501 · Functions from hypothesis.strategies will no longer raise InvalidAr‐
6503 gument on bad arguments. Instead the same errors will be raised when
6504 a test using such a strategy is run. This may improve startup time in
6505 some cases, but the main reason for it is so that errors in strate‐
6506 gies won't cause errors in loading, and it can interact correctly
6507 with things like pytest.mark.skipif.
6508 · Errors caused by accidentally invoking the legacy API are now much
6510 less confusing, although still throw NotImplementedError.
6511 · hypothesis.extra.django is 1.9 compatible.
6513 · When tests are run with max_shrinks=0 this will now still rerun the
6515 test on failure and will no longer print "Trying example:" before
6516 each run. Additionally note() will now work correctly when used with
6517 max_shrinks=0.
6518 1.15.0 - 2015-11-24
6520 A release with two new features.
6521 · A 'characters' strategy for more flexible generation of text with
6523 particular character ranges and types, kindly contributed by
6524 Alexander Shorin.
6525 · Add support for preconditions to the rule based stateful testing.
6527 Kindly contributed by Christopher Armstrong
6528 1.14.0 - 2015-11-01
6530 New features:
6531 · Add 'note' function which lets you include additional information in
6533 the final test run's output.
6534 · Add 'choices' strategy which gives you a choice function that emu‐
6536 lates random.choice.
6537 · Add 'uuid' strategy that generates UUIDs'
6539 · Add 'shared' strategy that lets you create a strategy that just gen‐
6541 erates a single shared value for each test run
6542 Bugs:
6544 · Using strategies of the form streaming(x.flatmap(f)) with find or in
6546 stateful testing would have caused InvalidArgument errors when the
6547 resulting values were used (because code that expected to only be
6548 called within a test context would be invoked).
6549 1.13.0 - 2015-10-29
6551 This is quite a small release, but deprecates some public API functions
6552 and removes some internal API functionality so gets a minor version
6553 bump.
6554 · All calls to the 'strategy' function are now deprecated, even ones
6556 which pass just a SearchStrategy instance (which is still a no-op).
6557 · Never documented hypothesis.extra entry_points mechanism has now been
6559 removed ( it was previously how hypothesis.extra packages were loaded
6560 and has been deprecated and unused for some time)
6561 · Some corner cases that could previously have produced an OverflowEr‐
6563 ror when simplifying failing cases using hypothesis.extra.datetimes
6564 (or dates or times) have now been fixed.
6565 · Hypothesis load time for first import has been significantly reduced
6567 - it used to be around 250ms (on my SSD laptop) and now is around
6568 100-150ms. This almost never matters but was slightly annoying when
6569 using it in the console.
6570 · hypothesis.strategies.randoms was previously missing from __all__.
6572 1.12.0 - 2015-10-18
6574 · Significantly improved performance of creating strategies using the
6575 functions from the hypothesis.strategies module by deferring the cal‐
6576 culation of their repr until it was needed. This is unlikely to have
6577 been an performance issue for you unless you were using flatmap, com‐
6578 posite or stateful testing, but for some cases it could be quite a
6579 significant impact.
6580 · A number of cases where the repr of strategies build from lambdas is
6582 improved
6583 · Add dates() and times() strategies to hypothesis.extra.datetimes
6585 · Add new 'profiles' mechanism to the settings system
6587 · Deprecates mutability of Settings, both the Settings.default top
6589 level property and individual settings.
6590 · A Settings object may now be directly initialized from a parent Set‐
6592 tings.
6593 · @given should now give a better error message if you attempt to use
6595 it with a function that uses destructuring arguments (it still won't
6596 work, but it will error more clearly),
6597 · A number of spelling corrections in error messages
6599 · py.test should no longer display the intermediate modules Hypothesis
6601 generates when running in verbose mode
6602 · Hypothesis should now correctly handle printing objects with
6604 non-ascii reprs on python 3 when running in a locale that cannot han‐
6605 dle ascii printing to stdout.
6606 · Add a unique=True argument to lists(). This is equivalent to
6608 unique_by=lambda x: x, but offers a more convenient syntax.
6609 1.11.4 - 2015-09-27
6611 · Hide modifications Hypothesis needs to make to sys.path by undoing
6612 them after we've imported the relevant modules. This is a workaround
6613 for issues cryptography experienced on windows.
6614 · Slightly improved performance of drawing from sampled_from on large
6616 lists of alternatives.
6617 · Significantly improved performance of drawing from one_of or strate‐
6619 gies using | (note this includes a lot of strategies internally -
6620 floats() and integers() both fall into this category). There turned
6621 out to be a massive performance regression introduced in 1.10.0
6622 affecting these which probably would have made tests using Hypothesis
6623 significantly slower than they should have been.
6624 1.11.3 - 2015-09-23
6626 · Better argument validation for datetimes() strategy - previously set‐
6627 ting max_year < datetime.MIN_YEAR or min_year > datetime.MAX_YEAR
6628 would not have raised an InvalidArgument error and instead would have
6629 behaved confusingly.
6630 · Compatibility with being run on pytest < 2.7 (achieved by disabling
6632 the plugin).
6633 1.11.2 - 2015-09-23
6635 Bug fixes:
6636 · Settings(database=my_db) would not be correctly inherited when used
6638 as a default setting, so that newly created settings would use the
6639 database_file setting and create an SQLite example database.
6640 · Settings.default.database = my_db would previously have raised an
6642 error and now works.
6643 · Timeout could sometimes be significantly exceeded if during simplifi‐
6645 cation there were a lot of examples tried that didn't trigger the
6646 bug.
6647 · When loading a heavily simplified example using a basic() strategy
6649 from the database this could cause Python to trigger a recursion
6650 error.
6651 · Remove use of deprecated API in pytest plugin so as to not emit warn‐
6653 ing
6654 Misc:
6656 · hypothesis-pytest is now part of hypothesis core. This should have no
6658 externally visible consequences, but you should update your dependen‐
6659 cies to remove hypothesis-pytest and depend on only Hypothesis.
6660 · Better repr for hypothesis.extra.datetimes() strategies.
6662 · Add .close() method to abstract base class for Backend (it was
6664 already present in the main implementation).
6665 1.11.1 - 2015-09-16
6667 Bug fixes:
6668 · When running Hypothesis tests in parallel (e.g. using pytest-xdist)
6670 there was a race condition caused by code generation.
6671 · Example databases are now cached per thread so as to not use sqlite
6673 connections from multiple threads. This should make Hypothesis now
6674 entirely thread safe.
6675 · floats() with only min_value or max_value set would have had a very
6677 bad distribution.
6678 · Running on 3.5, Hypothesis would have emitted deprecation warnings
6680 because of use of inspect.getargspec
6681 1.11.0 - 2015-08-31
6683 · text() with a non-string alphabet would have used the repr() of the
6684 the alphabet instead of its contexts. This is obviously silly. It now
6685 works with any sequence of things convertible to unicode strings.
6686 · @given will now work on methods whose definitions contains no
6688 explicit positional arguments, only varargs (issue #118). This may
6689 have some knock on effects because it means that @given no longer
6690 changes the argspec of functions other than by adding defaults.
6691 · Introduction of new @composite feature for more natural definition of
6693 strategies you'd previously have used flatmap for.
6694 1.10.6 - 2015-08-26
6696 Fix support for fixtures on Django 1.7.
6697 1.10.4 - 2015-08-21
6699 Tiny bug fix release:
6700 · If the database_file setting is set to None, this would have resulted
6702 in an error when running tests. Now it does the same as setting data‐
6703 base to None.
6704 1.10.3 - 2015-08-19
6706 Another small bug fix release.
6707 · lists(elements, unique_by=some_function, min_size=n) would have
6709 raised a ValidationError if n > Settings.default.average_list_length
6710 because it would have wanted to use an average list length shorter
6711 than the minimum size of the list, which is impossible. Now it
6712 instead defaults to twice the minimum size in these circumstances.
6713 · basic() strategy would have only ever produced at most ten distinct
6715 values per run of the test (which is bad if you e.g. have it inside a
6716 list). This was obviously silly. It will now produce a much better
6717 distribution of data, both duplicated and non duplicated.
6718 1.10.2 - 2015-08-19
6720 This is a small bug fix release:
6721 · star imports from hypothesis should now work correctly.
6723 · example quality for examples using flatmap will be better, as the way
6725 it had previously been implemented was causing problems where Hypoth‐
6726 esis was erroneously labelling some examples as being duplicates.
6727 1.10.0 - 2015-08-04
6729 This is just a bugfix and performance release, but it changes some
6730 semi-public APIs, hence the minor version bump.
6731 · Significant performance improvements for strategies which are
6733 one_of() many branches. In particular this included recursive()
6734 strategies. This should take the case where you use one recursive()
6735 strategy as the base strategy of another from unusably slow (tens of
6736 seconds per generated example) to reasonably fast.
6737 · Better handling of just() and sampled_from() for values which have an
6739 incorrect __repr__ implementation that returns non-ASCII unicode on
6740 Python 2.
6741 · Better performance for flatmap from changing the internal morpher API
6743 to be significantly less general purpose.
6744 · Introduce a new semi-public BuildContext/cleanup API. This allows
6746 strategies to register cleanup activities that should run once the
6747 example is complete. Note that this will interact somewhat weirdly
6748 with find.
6749 · Better simplification behaviour for streaming strategies.
6751 · Don't error on lambdas which use destructuring arguments in Python 2.
6753 · Add some better reprs for a few strategies that were missing good
6755 ones.
6756 · The Random instances provided by randoms() are now copyable.
6758 · Slightly more debugging information about simplify when using a debug
6760 verbosity level.
6761 · Support using given for functions with varargs, but not passing argu‐
6763 ments to it as positional.
6764 1.9.0 - 2015-07-27
6766 Codename: The great bundling.
6767 This release contains two fairly major changes.
6769 The first is the deprecation of the hypothesis-extra mechanism. From
6771 now on all the packages that were previously bundled under it other
6772 than hypothesis-pytest (which is a different beast and will remain sep‐
6773 arate). The functionality remains unchanged and you can still import
6774 them from exactly the same location, they just are no longer separate
6775 packages.
6776 The second is that this introduces a new way of building strategies
6778 which lets you build up strategies recursively from other strategies.
6779 It also contains the minor change that calling .example() on a strategy
6781 object will give you examples that are more representative of the
6782 actual data you'll get. There used to be some logic in there to make
6783 the examples artificially simple but this proved to be a bad idea.
6784 1.8.5 - 2015-07-24
6786 This contains no functionality changes but fixes a mistake made with
6787 building the previous package that would have broken installation on
6788 Windows.
6789 1.8.4 - 2015-07-20
6791 Bugs fixed:
6792 · When a call to floats() had endpoints which were not floats but
6794 merely convertible to one (e.g. integers), these would be included in
6795 the generated data which would cause it to generate non-floats.
6796 · Splitting lambdas used in the definition of flatmap, map or filter
6798 over multiple lines would break the repr, which would in turn break
6799 their usage.
6800 1.8.3 - 2015-07-20
6802 "Falsifying example" would not have been printed when the failure came
6803 from an explicit example.
6804 1.8.2 - 2015-07-18
6806 Another small bugfix release:
6807 · When using ForkingTestCase you would usually not get the falsifying
6809 example printed if the process exited abnormally (e.g. due to
6810 os._exit).
6811 · Improvements to the distribution of characters when using text() with
6813 a default alphabet. In particular produces a better distribution of
6814 ascii and whitespace in the alphabet.
6815 1.8.1 - 2015-07-17
6817 This is a small release that contains a workaround for people who have
6818 bad reprs returning non ascii text on Python 2.7. This is not a bug fix
6819 for Hypothesis per se because that's not a thing that is actually sup‐
6820 posed to work, but Hypothesis leans more heavily on repr than is typi‐
6821 cal so it's worth having a workaround for.
6822 1.8.0 - 2015-07-16
6824 New features:
6825 · Much more sensible reprs for strategies, especially ones that come
6827 from hypothesis.strategies. These should now have as reprs python
6828 code that would produce the same strategy.
6829 · lists() accepts a unique_by argument which forces the generated lists
6831 to be only contain elements unique according to some function key
6832 (which must return a hashable value).
6833 · Better error messages from flaky tests to help you debug things.
6835 Mostly invisible implementation details that may result in finding new
6837 bugs in your code:
6838 · Sets and dictionary generation should now produce a better range of
6840 results.
6841 · floats with bounds now focus more on 'critical values', trying to
6843 produce values at edge cases.
6844 · flatmap should now have better simplification for complicated cases,
6846 as well as generally being (I hope) more reliable.
6847 Bug fixes:
6849 · You could not previously use assume() if you were using the forking
6851 executor.
6852 1.7.2 - 2015-07-10
6854 This is purely a bug fix release:
6855 · When using floats() with stale data in the database you could some‐
6857 times get values in your tests that did not respect min_value or
6858 max_value.
6859 · When getting a Flaky error from an unreliable test it would have
6861 incorrectly displayed the example that caused it.
6862 · 2.6 dependency on backports was incorrectly specified. This would
6864 only have caused you problems if you were building a universal wheel
6865 from Hypothesis, which is not how Hypothesis ships, so unless you're
6866 explicitly building wheels for your dependencies and support Python
6867 2.6 plus a later version of Python this probably would never have
6868 affected you.
6869 · If you use flatmap in a way that the strategy on the right hand side
6871 depends sensitively on the left hand side you may have occasionally
6872 seen Flaky errors caused by producing unreliable examples when mini‐
6873 mizing a bug. This use case may still be somewhat fraught to be hon‐
6874 est. This code is due a major rearchitecture for 1.8, but in the
6875 meantime this release fixes the only source of this error that I'm
6876 aware of.
6877 1.7.1 - 2015-06-29
6879 Codename: There is no 1.7.0.
6880 A slight technical hitch with a premature upload means there's was a
6882 yanked 1.7.0 release. Oops.
6883 The major feature of this release is Python 2.6 support. Thanks to Jeff
6885 Meadows for doing most of the work there.
6886 Other minor features
6888 · strategies now has a permutations() function which returns a strategy
6890 yielding permutations of values from a given collection.
6891 · if you have a flaky test it will print the exception that it last saw
6893 before failing with Flaky, even if you do not have verbose reporting
6894 on.
6895 · Slightly experimental git merge script available as "python -m
6897 hypothesis.tools.mergedbs". Instructions on how to use it in the doc‐
6898 string of that file.
6899 Bug fixes:
6901 · Better performance from use of filter. In particular tests which
6903 involve large numbers of heavily filtered strategies should perform a
6904 lot better.
6905 · floats() with a negative min_value would not have worked correctly
6907 (worryingly, it would have just silently failed to run any examples).
6908 This is now fixed.
6909 · tests using sampled_from would error if the number of sampled ele‐
6911 ments was smaller than min_satisfying_examples.
6912 1.6.2 - 2015-06-08
6914 This is just a few small bug fixes:
6915 · Size bounds were not validated for values for a binary() strategy
6917 when reading examples from the database.
6918 · sampled_from is now in __all__ in hypothesis.strategies
6920 · floats no longer consider negative integers to be simpler than posi‐
6922 tive non-integers
6923 · Small floating point intervals now correctly count members, so if you
6925 have a floating point interval so narrow there are only a handful of
6926 values in it, this will no longer cause an error when Hypothesis runs
6927 out of values.
6928 1.6.1 - 2015-05-21
6930 This is a small patch release that fixes a bug where 1.6.0 broke the
6931 use of flatmap with the deprecated API and assumed the passed in func‐
6932 tion returned a SearchStrategy instance rather than converting it to a
6933 strategy.
6934 1.6.0 - 2015-05-21
6936 This is a smallish release designed to fix a number of bugs and smooth
6937 out some weird behaviours.
6938 · Fix a critical bug in flatmap where it would reuse old strategies. If
6940 all your flatmap code was pure you're fine. If it's not, I'm sur‐
6941 prised it's working at all. In particular if you want to use flatmap
6942 with django models, you desperately need to upgrade to this version.
6943 · flatmap simplification performance should now be better in some cases
6945 where it previously had to redo work.
6946 · Fix for a bug where invalid unicode data with surrogates could be
6948 generated during simplification (it was already filtered out during
6949 actual generation).
6950 · The Hypothesis database is now keyed off the name of the test instead
6952 of the type of data. This makes much more sense now with the new
6953 strategies API and is generally more robust. This means you will lose
6954 old examples on upgrade.
6955 · The database will now not delete values which fail to deserialize
6957 correctly, just skip them. This is to handle cases where multiple
6958 incompatible strategies share the same key.
6959 · find now also saves and loads values from the database, keyed off a
6961 hash of the function you're finding from.
6962 · Stateful tests now serialize and load values from the database. They
6964 should have before, really. This was a bug.
6965 · Passing a different verbosity level into a test would not have worked
6967 entirely correctly, leaving off some messages. This is now fixed.
6968 · Fix a bug where derandomized tests with unicode characters in the
6970 function body would error on Python 2.7.
6971 1.5.0 - 2015-05-14
6973 Codename: Strategic withdrawal.
6974 The purpose of this release is a radical simplification of the API for
6976 building strategies. Instead of the old approach of @strategy.extend
6977 and things that get converted to strategies, you just build strategies
6978 directly.
6979 The old method of defining strategies will still work until Hypothesis
6981 2.0, because it's a major breaking change, but will now emit depreca‐
6982 tion warnings.
6983 The new API is also a lot more powerful as the functions for defining
6985 strategies give you a lot of dials to turn. See the updated data sec‐
6986 tion for details.
6987 Other changes:
6989 · Mixing keyword and positional arguments in a call to @given is
6991 deprecated as well.
6992 · There is a new setting called 'strict'. When set to True, Hypothe‐
6994 sis will raise warnings instead of merely printing them. Turning
6995 it on by default is inadvisable because it means that Hypothesis
6996 minor releases can break your code, but it may be useful for mak‐
6997 ing sure you catch all uses of deprecated APIs.
6998 · max_examples in settings is now interpreted as meaning the maximum
7000 number of unique (ish) examples satisfying assumptions. A new set‐
7001 ting max_iterations which defaults to a larger value has the old
7002 interpretation.
7003 · Example generation should be significantly faster due to a new
7005 faster parameter selection algorithm. This will mostly show up for
7006 simple data types - for complex ones the parameter selection is
7007 almost certainly dominated.
7008 · Simplification has some new heuristics that will tend to cut down
7010 on cases where it could previously take a very long time.
7011 · timeout would previously not have been respected in cases where
7013 there were a lot of duplicate examples. You probably wouldn't have
7014 previously noticed this because max_examples counted duplicates,
7015 so this was very hard to hit in a way that mattered.
7016 · A number of internal simplifications to the SearchStrategy API.
7018 · You can now access the current Hypothesis version as hypothe‐
7020 sis.__version__.
7021 · A top level function is provided for running the stateful tests
7023 without the TestCase infrastructure.
7024 1.4.0 - 2015-05-04
7026 Codename: What a state.
7027 The big feature of this release is the new and slightly experimental
7029 stateful testing API. You can read more about that in the appropriate
7030 section.
7031 Two minor features the were driven out in the course of developing
7033 this:
7034 · You can now set settings.max_shrinks to limit the number of times
7036 Hypothesis will try to shrink arguments to your test. If this is set
7037 to <= 0 then Hypothesis will not rerun your test and will just raise
7038 the failure directly. Note that due to technical limitations if
7039 max_shrinks is <= 0 then Hypothesis will print every example it calls
7040 your test with rather than just the failing one. Note also that I
7041 don't consider settings max_shrinks to zero a sensible way to run
7042 your tests and it should really be considered a debug feature.
7043 · There is a new debug level of verbosity which is even more verbose
7045 than verbose. You probably don't want this.
7046 Breakage of semi-public SearchStrategy API:
7048 · It is now a required invariant of SearchStrategy that if u simplifies
7050 to v then it is not the case that strictly_simpler(u, v). i.e. sim‐
7051 plifying should not increase the complexity even though it is not
7052 required to decrease it. Enforcing this invariant lead to finding
7053 some bugs where simplifying of integers, floats and sets was subopti‐
7054 mal.
7055 · Integers in basic data are now required to fit into 64 bits. As a
7057 result python integer types are now serialized as strings, and some
7058 types have stopped using quite so needlessly large random seeds.
7059 Hypothesis Stateful testing was then turned upon Hypothesis itself,
7061 which lead to an amazing number of minor bugs being found in Hypothesis
7062 itself.
7063 Bugs fixed (most but not all from the result of stateful testing)
7065 include:
7066 · Serialization of streaming examples was flaky in a way that you would
7068 probably never notice: If you generate a template, simplify it, seri‐
7069 alize it, deserialize it, serialize it again and then deserialize it
7070 you would get the original stream instead of the simplified one.
7071 · If you reduced max_examples below the number of examples already
7073 saved in the database, you would have got a ValueError. Additionally,
7074 if you had more than max_examples in the database all of them would
7075 have been considered.
7076 · @given will no longer count duplicate examples (which it never called
7078 your function with) towards max_examples. This may result in your
7079 tests running slower, but that's probably just because they're trying
7080 more examples.
7081 · General improvements to example search which should result in better
7083 performance and higher quality examples. In particular parameters
7084 which have a history of producing useless results will be more
7085 aggressively culled. This is useful both because it decreases the
7086 chance of useless examples and also because it's much faster to not
7087 check parameters which we were unlikely to ever pick!
7088 · integers_from and lists of types with only one value (e.g. [None])
7090 would previously have had a very high duplication rate so you were
7091 probably only getting a handful of examples. They now have a much
7092 lower duplication rate, as well as the improvements to search making
7093 this less of a problem in the first place.
7094 · You would sometimes see simplification taking significantly longer
7096 than your defined timeout. This would happen because timeout was only
7097 being checked after each successful simplification, so if Hypothesis
7098 was spending a lot of time unsuccessfully simplifying things it
7099 wouldn't stop in time. The timeout is now applied for unsuccessful
7100 simplifications too.
7101 · In Python 2.7, integers_from strategies would have failed during sim‐
7103 plification with an OverflowError if their starting point was at or
7104 near to the maximum size of a 64-bit integer.
7105 · flatmap and map would have failed if called with a function without a
7107 __name__ attribute.
7108 · If max_examples was less than min_satisfying_examples this would
7110 always error. Now min_satisfying_examples is capped to max_examples.
7111 Note that if you have assumptions to satisfy here this will still
7112 cause an error.
7113 Some minor quality improvements:
7115 · Lists of streams, flatmapped strategies and basic strategies should
7117 now now have slightly better simplification.
7118 1.3.0 - 2015-05-22
7120 New features:
7121 · New verbosity level API for printing intermediate results and excep‐
7123 tions.
7124 · New specifier for strings generated from a specified alphabet.
7126 · Better error messages for tests that are failing because of a lack of
7128 enough examples.
7129 Bug fixes:
7131 · Fix error where use of ForkingTestCase would sometimes result in too
7133 many open files.
7134 · Fix error where saving a failing example that used flatmap could
7136 error.
7137 · Implement simplification for sampled_from, which apparently never
7139 supported it previously. Oops.
7140 General improvements:
7142 · Better range of examples when using one_of or sampled_from.
7144 · Fix some pathological performance issues when simplifying lists of
7146 complex values.
7147 · Fix some pathological performance issues when simplifying examples
7149 that require unicode strings with high codepoints.
7150 · Random will now simplify to more readable examples.
7152 1.2.1 - 2015-04-16
7154 A small patch release for a bug in the new executors feature. Tests
7155 which require doing something to their result in order to fail would
7156 have instead reported as flaky.
7157 1.2.0 - 2015-04-15
7159 Codename: Finders keepers.
7160 A bunch of new features and improvements.
7162 · Provide a mechanism for customizing how your tests are executed.
7164 · Provide a test runner that forks before running each example. This
7166 allows better support for testing native code which might trigger a
7167 segfault or a C level assertion failure.
7168 · Support for using Hypothesis to find examples directly rather than as
7170 just as a test runner.
7171 · New streaming type which lets you generate infinite lazily loaded
7173 streams of data - perfect for if you need a number of examples but
7174 don't know how many.
7175 · Better support for large integer ranges. You can now use inte‐
7177 gers_in_range with ranges of basically any size. Previously large
7178 ranges would have eaten up all your memory and taken forever.
7179 · Integers produce a wider range of data than before - previously they
7181 would only rarely produce integers which didn't fit into a machine
7182 word. Now it's much more common. This percolates to other numeric
7183 types which build on integers.
7184 · Better validation of arguments to @given. Some situations that would
7186 previously have caused silently wrong behaviour will now raise an
7187 error.
7188 · Include +/- sys.float_info.max in the set of floating point edge
7190 cases that Hypothesis specifically tries.
7191 · Fix some bugs in floating point ranges which happen when given +/-
7193 sys.float_info.max as one of the endpoints... (really any two floats
7194 that are sufficiently far apart so that x, y are finite but y - x is
7195 infinite). This would have resulted in generating infinite values
7196 instead of ones inside the range.
7197 1.1.1 - 2015-04-07
7199 Codename: Nothing to see here
7200 This is just a patch release put out because it fixed some internal
7202 bugs that would block the Django integration release but did not actu‐
7203 ally affect anything anyone could previously have been using. It also
7204 contained a minor quality fix for floats that I'd happened to have fin‐
7205 ished in time.
7206 · Fix some internal bugs with object lifecycle management that were
7208 impossible to hit with the previously released versions but broke
7209 hypothesis-django.
7210 · Bias floating point numbers somewhat less aggressively towards very
7212 small numbers
7213 1.1.0 - 2015-04-06
7215 Codename: No-one mention the M word.
7216 · Unicode strings are more strongly biased towards ascii characters.
7218 Previously they would generate all over the space. This is mostly so
7219 that people who try to shape their unicode strings with assume() have
7220 less of a bad time.
7221 · A number of fixes to data deserialization code that could theoreti‐
7223 cally have caused mysterious bugs when using an old version of a
7224 Hypothesis example database with a newer version. To the best of my
7225 knowledge a change that could have triggered this bug has never actu‐
7226 ally been seen in the wild. Certainly no-one ever reported a bug of
7227 this nature.
7228 · Out of the box support for Decimal and Fraction.
7230 · new dictionary specifier for dictionaries with variable keys.
7232 · Significantly faster and higher quality simplification, especially
7234 for collections of data.
7235 · New filter() and flatmap() methods on Strategy for better ways of
7237 building strategies out of other strategies.
7238 · New BasicStrategy class which allows you to define your own strate‐
7240 gies from scratch without needing an existing matching strategy or
7241 being exposed to the full horror or non-public nature of the Search‐
7242 Strategy interface.
7243 1.0.0 - 2015-03-27
7245 Codename: Blast-off!
7246 There are no code changes in this release. This is precisely the 0.9.2
7248 release with some updated documentation.
7249 0.9.2 - 2015-03-26
7251 Codename: T-1 days.
7252 · floats_in_range would not actually have produced floats_in_range
7254 unless that range happened to be (0, 1). Fix this.
7255 0.9.1 - 2015-03-25
7257 Codename: T-2 days.
7258 · Fix a bug where if you defined a strategy using map on a lambda then
7260 the results would not be saved in the database.
7261 · Significant performance improvements when simplifying examples using
7263 lists, strings or bounded integer ranges.
7264 0.9.0 - 2015-03-23
7266 Codename: The final countdown
7267 This release could also be called 1.0-RC1.
7269 It contains a teeny tiny bugfix, but the real point of this release is
7271 to declare feature freeze. There will be zero functionality changes
7272 between 0.9.0 and 1.0 unless something goes really really wrong. No new
7273 features will be added, no breaking API changes will occur, etc. This
7274 is the final shakedown before I declare Hypothesis stable and ready to
7275 use and throw a party to celebrate.
7276 Bug bounty for any bugs found between now and 1.0: I will buy you a
7278 drink (alcoholic, caffeinated, or otherwise) and shake your hand should
7279 we ever find ourselves in the same city at the same time.
7280 The one tiny bugfix:
7282 · Under pypy, databases would fail to close correctly when garbage col‐
7284 lected, leading to a memory leak and a confusing error message if you
7285 were repeatedly creating databases and not closing them. It is very
7286 unlikely you were doing this and the chances of you ever having
7287 noticed this bug are very low.
7288 0.7.2 - 2015-03-22
7290 Codename: Hygienic macros or bust
7291 · You can now name an argument to @given 'f' and it won't break (issue
7293 #38)
7294 · strategy_test_suite is now named strategy_test_suite as the documen‐
7296 tation claims and not in fact strategy_test_suitee
7297 · Settings objects can now be used as a context manager to temporarily
7299 override the default values inside their context.
7300 0.7.1 - 2015-03-21
7302 Codename: Point releases go faster
7303 · Better string generation by parametrizing by a limited alphabet
7305 · Faster string simplification - previously if simplifying a string
7307 with high range unicode characters it would try every unicode charac‐
7308 ter smaller than that. This was pretty pointless. Now it stops after
7309 it's a short range (it can still reach smaller ones through recursive
7310 calls because of other simplifying operations).
7311 · Faster list simplification by first trying a binary chop down the
7313 middle
7314 · Simultaneous simplification of identical elements in a list. So if a
7316 bug only triggers when you have duplicates but you drew e.g. [-17,
7317 -17], this will now simplify to [0, 0].
7318 0.7.0, - 2015-03-20
7320 Codename: Starting to look suspiciously real
7321 This is probably the last minor release prior to 1.0. It consists of
7323 stability improvements, a few usability things designed to make Hypoth‐
7324 esis easier to try out, and filing off some final rough edges from the
7325 API.
7326 · Significant speed and memory usage improvements
7328 · Add an example() method to strategy objects to give an example of the
7330 sort of data that the strategy generates.
7331 · Remove .descriptor attribute of strategies
7333 · Rename descriptor_test_suite to strategy_test_suite
7335 · Rename the few remaining uses of descriptor to specifier (descriptor
7337 already has a defined meaning in Python)
7338 0.6.0 - 2015-03-13
7340 Codename: I'm sorry, were you using that API?
7341 This is primarily a "simplify all the weird bits of the API" release.
7343 As a result there are a lot of breaking changes. If you just use @given
7344 with core types then you're probably fine.
7345 In particular:
7347 · Stateful testing has been removed from the API
7349 · The way the database is used has been rendered less useful (sorry).
7351 The feature for reassembling values saved from other tests doesn't
7352 currently work. This will probably be brought back in post 1.0.
7353 · SpecificationMapper is no longer a thing. Instead there is an
7355 ExtMethod called strategy which you extend to specify how to convert
7356 other types to strategies.
7357 · Settings are now extensible so you can add your own for configuring a
7359 strategy
7360 · MappedSearchStrategy no longer needs an unpack method
7362 · Basically all the SearchStrategy internals have changed massively. If
7364 you implemented SearchStrategy directly rather than using Mapped‐
7365 SearchStrategy talk to me about fixing it.
7366 · Change to the way extra packages work. You now specify the package.
7368 This must have a load() method. Additionally any modules in the pack‐
7369 age will be loaded in under hypothesis.extra
7370 Bug fixes:
7372 · Fix for a bug where calling falsify on a lambda with a non-ascii
7374 character in its body would error.
7375 Hypothesis Extra:
7377 hypothesis-fakefactory: An extension for using faker data in hypothe‐
7379 sis. Depends
7380 on fake-factory.
7381 0.5.0 - 2015-02-10
7383 Codename: Read all about it.
7384 Core hypothesis:
7386 · Add support back in for pypy and python 3.2
7388 · @given functions can now be invoked with some arguments explicitly
7390 provided. If all arguments that hypothesis would have provided are
7391 passed in then no falsification is run.
7392 · Related to the above, this means that you can now use pytest fixtures
7394 and mark.parametrize with Hypothesis without either interfering with
7395 the other.
7396 · Breaking change: @given no longer works for functions with varargs
7398 (varkwargs are fine). This might be added back in at a later date.
7399 · Windows is now fully supported. A limited version (just the tests
7401 with none of the extras) of the test suite is run on windows with
7402 each commit so it is now a first class citizen of the Hypothesis
7403 world.
7404 · Fix a bug for fuzzy equality of equal complex numbers with different
7406 reprs (this can happen when one coordinate is zero). This shouldn't
7407 affect users - that feature isn't used anywhere public facing.
7408 · Fix generation of floats on windows and 32-bit builds of python. I
7410 was using some struct.pack logic that only worked on certain word
7411 sizes.
7412 · When a test times out and hasn't produced enough examples this now
7414 raises a Timeout subclass of Unfalsifiable.
7415 · Small search spaces are better supported. Previously something like a
7417 @given(bool, bool) would have failed because it couldn't find enough
7418 examples. Hypothesis is now aware of the fact that these are small
7419 search spaces and will not error in this case.
7420 · Improvements to parameter search in the case of hard to satisfy
7422 assume. Hypothesis will now spend less time exploring parameters that
7423 are unlikely to provide anything useful.
7424 · Increase chance of generating "nasty" floats
7426 · Fix a bug that would have caused unicode warnings if you had a sam‐
7428 pled_from that was mixing unicode and byte strings.
7429 · Added a standard test suite that you can use to validate a custom
7431 strategy you've defined is working correctly.
7432 Hypothesis extra:
7434 First off, introducing Hypothesis extra packages!
7436 These are packages that are separated out from core Hypothesis because
7438 they have one or more dependencies. Every hypothesis-extra package is
7439 pinned to a specific point release of Hypothesis and will have some
7440 version requirements on its dependency. They use entry_points so you
7441 will usually not need to explicitly import them, just have them
7442 installed on the path.
7443 This release introduces two of them:
7445 hypothesis-datetime:
7447 Does what it says on the tin: Generates datetimes for Hypothesis. Just
7449 install the package and datetime support will start working.
7450 Depends on pytz for timezone support
7452 hypothesis-pytest:
7454 A very rudimentary pytest plugin. All it does right now is hook the
7456 display of falsifying examples into pytest reporting.
7457 Depends on pytest.
7459 0.4.3 - 2015-02-05
7461 Codename: TIL narrow Python builds are a thing
7462 This just fixes the one bug.
7464 · Apparently there is such a thing as a "narrow python build" and OS X
7466 ships with these by default for python 2.7. These are builds where
7467 you only have two bytes worth of unicode. As a result, generating
7468 unicode was completely broken on OS X. Fix this by only generating
7469 unicode codepoints in the range supported by the system.
7470 0.4.2 - 2015-02-04
7472 Codename: O(dear)
7473 This is purely a bugfix release:
7475 · Provide sensible external hashing for all core types. This will sig‐
7477 nificantly improve performance of tracking seen examples which hap‐
7478 pens in literally every falsification run. For Hypothesis fixing this
7479 cut 40% off the runtime of the test suite. The behaviour is quadratic
7480 in the number of examples so if you're running the default configura‐
7481 tion this will be less extreme (Hypothesis's test suite runs at a
7482 higher number of examples than default), but you should still see a
7483 significant improvement.
7484 · Fix a bug in formatting of complex numbers where the string could get
7486 incorrectly truncated.
7487 0.4.1 - 2015-02-03
7489 Codename: Cruel and unusual edge cases
7490 This release is mostly about better test case generation.
7492 Enhancements:
7494 · Has a cool release name
7496 · text_type (str in python 3, unicode in python 2) example generation
7498 now actually produces interesting unicode instead of boring ascii
7499 strings.
7500 · floating point numbers are generated over a much wider range, with
7502 particular attention paid to generating nasty numbers - nan, infin‐
7503 ity, large and small values, etc.
7504 · examples can be generated using pieces of examples previously saved
7506 in the database. This allows interesting behaviour that has previ‐
7507 ously been discovered to be propagated to other examples.
7508 · improved parameter exploration algorithm which should allow it to
7510 more reliably hit interesting edge cases.
7511 · Timeout can now be disabled entirely by setting it to any value <= 0.
7513 Bug fixes:
7515 · The descriptor on a OneOfStrategy could be wrong if you had descrip‐
7517 tors which were equal but should not be coalesced. e.g. a strategy
7518 for one_of((frozenset({int}), {int})) would have reported its
7519 descriptor as {int}. This is unlikely to have caused you any problems
7520 · If you had strategies that could produce NaN (which float previously
7522 couldn't but e.g. a Just(float('nan')) could) then this would have
7523 sent hypothesis into an infinite loop that would have only been ter‐
7524 minated when it hit the timeout.
7525 · Given elements that can take a long time to minimize, minimization of
7527 floats or tuples could be quadratic or worse in the that value. You
7528 should now see much better performance for simplification, albeit at
7529 some cost in quality.
7530 Other:
7532 · A lot of internals have been been rewritten. This shouldn't affect
7534 you at all, but it opens the way for certain of hypothesis's oddities
7535 to be a lot more extensible by users. Whether this is a good thing
7536 may be up for debate...
7537 0.4.0 - 2015-01-21
7539 FLAGSHIP FEATURE: Hypothesis now persists examples for later use. It
7540 stores data in a local SQLite database and will reuse it for all tests
7541 of the same type.
7542 LICENSING CHANGE: Hypothesis is now released under the Mozilla Public
7544 License 2.0. This applies to all versions from 0.4.0 onwards until fur‐
7545 ther notice. The previous license remains applicable to all code prior
7546 to 0.4.0.
7547 Enhancements:
7549 · Printing of failing examples. I was finding that the pytest runner
7551 was not doing a good job of displaying these, and that Hypothesis
7552 itself could do much better.
7553 · Drop dependency on six for cross-version compatibility. It was easy
7555 enough to write the shim for the small set of features that we care
7556 about and this lets us avoid a moderately complex dependency.
7557 · Some improvements to statistical distribution of selecting from small
7559 (<= 3 elements)
7560 · Improvements to parameter selection for finding examples.
7562 Bugs fixed:
7564 · could_have_produced for lists, dicts and other collections would not
7566 have examined the elements and thus when using a union of different
7567 types of list this could result in Hypothesis getting confused and
7568 passing a value to the wrong strategy. This could potentially result
7569 in exceptions being thrown from within simplification.
7570 · sampled_from would not work correctly on a single element list.
7572 · Hypothesis could get very confused by values which are equal despite
7574 having different types being used in descriptors. Hypothesis now has
7575 its own more specific version of equality it uses for descriptors and
7576 tracking. It is always more fine grained than Python equality: Things
7577 considered != are not considered equal by hypothesis, but some things
7578 that are considered == are distinguished. If your test suite uses
7579 both frozenset and set tests this bug is probably affecting you.
7580 0.3.2 - 2015-01-16
7582 · Fix a bug where if you specified floats_in_range with integer argu‐
7583 ments Hypothesis would error in example simplification.
7584 · Improve the statistical distribution of the floats you get for the
7586 floats_in_range strategy. I'm not sure whether this will affect users
7587 in practice but it took my tests for various conditions from flaky to
7588 rock solid so it at the very least improves discovery of the artifi‐
7589 cial cases I'm looking for.
7590 · Improved repr() for strategies and RandomWithSeed instances.
7592 · Add detection for flaky test cases where hypothesis managed to find
7594 an example which breaks it but on the final invocation of the test it
7595 does not raise an error. This will typically happen with too much
7596 recursion errors but could conceivably happen in other circumstances
7597 too.
7598 · Provide a "derandomized" mode. This allows you to run hypothesis with
7600 zero real randomization, making your build nice and deterministic.
7601 The tests run with a seed calculated from the function they're test‐
7602 ing so you should still get a good distribution of test cases.
7603 · Add a mechanism for more conveniently defining tests which just sam‐
7605 ple from some collection.
7606 · Fix for a really subtle bug deep in the internals of the strategy ta‐
7608 ble. In some circumstances if you were to define instance strategies
7609 for both a parent class and one or more of its subclasses you would
7610 under some circumstances get the strategy for the wrong superclass of
7611 an instance. It is very unlikely anyone has ever encountered this in
7612 the wild, but it is conceivably possible given that a mix of namedtu‐
7613 ple and tuple are used fairly extensively inside hypothesis which do
7614 exhibit this pattern of strategy.
7615 0.3.1 - 2015-01-13
7617 · Support for generation of frozenset and Random values
7618 · Correct handling of the case where a called function mutates it argu‐
7620 ment. This involved introducing a notion of a strategies knowing how
7621 to copy their argument. The default method should be entirely accept‐
7622 able and the worst case is that it will continue to have the old be‐
7623 haviour if you don't mark your strategy as mutable, so this shouldn't
7624 break anything.
7625 · Fix for a bug where some strategies did not correctly implement
7627 could_have_produced. It is very unlikely that any of these would have
7628 been seen in the wild, and the consequences if they had been would
7629 have been minor.
7630 · Re-export the @given decorator from the main hypothesis namespace.
7632 It's still available at the old location too.
7633 · Minor performance optimisation for simplifying long lists.
7635 0.3.0 - 2015-01-12
7637 · Complete redesign of the data generation system. Extreme breaking
7638 change for anyone who was previously writing their own SearchStrategy
7639 implementations. These will not work any more and you'll need to mod‐
7640 ify them.
7641 · New settings system allowing more global and modular control of Veri‐
7643 fier behaviour.
7644 · Decouple SearchStrategy from the StrategyTable. This leads to much
7646 more composable code which is a lot easier to understand.
7647 · A significant amount of internal API renaming and moving. This may
7649 also break your code.
7650 · Expanded available descriptors, allowing for generating integers or
7652 floats in a specific range.
7653 · Significantly more robust. A very large number of small bug fixes,
7655 none of which anyone is likely to have ever noticed.
7656 · Deprecation of support for pypy and python 3 prior to 3.3. 3.3 and
7658 3.4. Supported versions are 2.7.x, 3.3.x, 3.4.x. I expect all of
7659 these to remain officially supported for a very long time. I would
7660 not be surprised to add pypy support back in later but I'm not going
7661 to do so until I know someone cares about it. In the meantime it will
7662 probably still work.
7663 0.2.2 - 2015-01-08
7665 · Fix an embarrassing complete failure of the installer caused by my
7666 being bad at version control
7667 0.2.1 - 2015-01-07
7669 · Fix a bug in the new stateful testing feature where you could make
7670 __init__ a @requires method. Simplification would not always work if
7671 the prune method was able to successfully shrink the test.
7672 0.2.0 - 2015-01-07
7674 · It's aliiive.
7675 · Improve python 3 support using six.
7677 · Distinguish between byte and unicode types.
7679 · Fix issues where FloatStrategy could raise.
7681 · Allow stateful testing to request constructor args.
7683 · Fix for issue where test annotations would timeout based on when the
7685 module was loaded instead of when the test started
7686 0.1.4 - 2013-12-14
7688 · Make verification runs time bounded with a configurable timeout
7689 0.1.3 - 2013-05-03
7691 · Bugfix: Stateful testing behaved incorrectly with subclassing.
7692 · Complex number support
7694 · support for recursive strategies
7696 · different error for hypotheses with unsatisfiable assumptions
7698 0.1.2 - 2013-03-24
7700 · Bugfix: Stateful testing was not minimizing correctly and could throw
7701 exceptions.
7702 · Better support for recursive strategies.
7704 · Support for named tuples.
7706 · Much faster integer generation.
7708 0.1.1 - 2013-03-24
7710 · Python 3.x support via 2to3.
7711 · Use new style classes (oops).
7713 0.1.0 - 2013-03-23
7715 · Introduce stateful testing.
7716 · Massive rewrite of internals to add flags and strategies.
7718 0.0.5 - 2013-03-13
7720 · No changes except trying to fix packaging
7721 0.0.4 - 2013-03-13
7723 · No changes except that I checked in a failing test case for 0.0.3 so
7724 had to replace the release. Doh
7725 0.0.3 - 2013-03-13
7727 · Improved a few internals.
7728 · Opened up creating generators from instances as a general API.
7730 · Test integration.
7732 0.0.2 - 2013-03-12
7734 · Starting to tighten up on the internals.
7735 · Change API to allow more flexibility in configuration.
7737 · More testing.
7739 0.0.1 - 2013-03-10
7741 · Initial release.
7742 · Basic working prototype. Demonstrates idea, probably shouldn't be
7744 used.
7745
7747 Hypothesis development is managed by me, David R. MacIver. I am the
7748 primary author of Hypothesis.
7749 However, I no longer do unpaid feature development on Hypothesis. My
7751 roles as leader of the project are:
7752 1. Helping other people do feature development on Hypothesis
7754 2. Fixing bugs and other code health issues
7756 3. Improving documentation
7758 4. General release management work
7760 5. Planning the general roadmap of the project
7762 6. Doing sponsored development on tasks that are too large or in depth
7764 for other people to take on
7765 So all new features must either be sponsored or implemented by someone
7767 else. That being said, the maintenance team takes an active role in
7768 shepherding pull requests and helping people write a new feature (see
7769 CONTRIBUTING.rst for details and pull request #154 for an example of
7770 how the process goes). This isn't "patches welcome", it's "we will help
7771 you write a patch".
7772 Release Policy
7774 Hypothesis releases follow semantic versioning.
7775 We maintain backwards-compatibility wherever possible, and use depreca‐
7777 tion warnings to mark features that have been superseded by a newer
7778 alternative. If you want to detect this, you can upgrade warnings to
7779 errors in the usual ways.
7780 We use continuous deployment to ensure that you can always use our new‐
7782 est and shiniest features - every change to the source tree is automat‐
7783 ically built and published on PyPI as soon as it's merged onto master,
7784 after code review and passing our extensive test suite.
7785 Project Roadmap
7787 Hypothesis does not have a long-term release plan. However some visi‐
7788 bility into our plans for future compatibility may be useful:
7789 · We value compatibility, and maintain it as far as practical. This
7791 generally excludes things which are end-of-life upstream, or have an
7792 unstable API.
7793 · We would like to drop Python 2 support when it reaches end of life in
7795 2020. Ongoing support is likely to depend on commercial funding.
7796 · We intend to support PyPy3 as soon as it supports a recent enough
7798 version of Python 3. See issue #602.
7799
7801 For questions you are happy to ask in public, the Hypothesis community
7802 is a friendly place where I or others will be more than happy to help
7803 you out. You're also welcome to ask questions on Stack Overflow. If you
7804 do, please tag them with 'python-hypothesis' so someone sees them.
7805 For bugs and enhancements, please file an issue on the GitHub issue
7807 tracker. Note that as per the development policy, enhancements will
7808 probably not get implemented unless you're willing to pay for develop‐
7809 ment or implement them yourself (with assistance from me). Bugs will
7810 tend to get fixed reasonably promptly, though it is of course on a best
7811 effort basis.
7812 To see the versions of Python, optional dependencies, test runners, and
7814 operating systems Hypothesis supports (meaning incompatibility is
7815 treated as a bug), see supported.
7816 If you need to ask questions privately or want more of a guarantee of
7818 bugs being fixed promptly, please contact me on
7819 hypothesis-support@drmaciver.com to talk about availability of support
7820 contracts.
7821
7823 Downstream packagers often want to package Hypothesis. Here are some
7824 guidelines.
7825 The primary guideline is this: If you are not prepared to keep up with
7827 the Hypothesis release schedule, don't. You will annoy me and are doing
7828 your users a disservice.
7829 Hypothesis has a very frequent release schedule. It's rare that it goes
7831 a week without a release, and there are often multiple releases in a
7832 given week.
7833 If you are prepared to keep up with this schedule, you might find the
7835 rest of this document useful.
7836 Release tarballs
7838 These are available from the GitHub releases page. The tarballs on pypi
7839 are intended for installation from a Python tool such as pip and should
7840 not be considered complete releases. Requests to include additional
7841 files in them will not be granted. Their absence is not a bug.
7842 Dependencies
7844 Python versions
7845 Hypothesis is designed to work with a range of Python versions. Cur‐
7846 rently supported are:
7847 · pypy-2.6.1 (earlier versions of pypy may work)
7849 · CPython 2.7.x
7851 · CPython 3.4.x
7853 · CPython 3.5.x
7855 · CPython 3.6.x
7857 · CPython 3.7.x
7859 If you feel the need to have separate Python 3 and Python 2 packages
7861 you can, but Hypothesis works unmodified on either.
7862 Other Python libraries
7864 Hypothesis has mandatory dependencies on the following libraries:
7865 · attrs
7867 · coverage
7869 · enum34 is required on Python 2.7
7871 Hypothesis has optional dependencies on the following libraries:
7873 · pytz (almost any version should work)
7875 · Faker, version 0.7 or later
7877 · Django, all supported versions
7879 · numpy, 1.10 or later (earlier versions will probably work fine)
7881 · pandas, 1.19 or later
7883 · pytest (3.0 or greater). This is a mandatory dependency for testing
7885 Hypothesis itself but optional for users.
7886 The way this works when installing Hypothesis normally is that these
7888 features become available if the relevant library is installed.
7889 Testing Hypothesis
7891 If you want to test Hypothesis as part of your packaging you will prob‐
7892 ably not want to use the mechanisms Hypothesis itself uses for running
7893 its tests, because it has a lot of logic for installing and testing
7894 against different versions of Python.
7895 The tests must be run with py.test. A version more recent than 2.8.0 is
7897 strongly encouraged, but it may work with earlier versions (however
7898 py.test specific logic is disabled before 2.8.0).
7899 Tests are organised into a number of top level subdirectories of the
7901 tests/ directory.
7902 · cover: This is a small, reasonably fast, collection of tests designed
7904 to give 100% coverage of all but a select subset of the files when
7905 run under Python 3.
7906 · nocover: This is a much slower collection of tests that should not be
7908 run under coverage for performance reasons.
7909 · py2: Tests that can only be run under Python 2
7911 · py3: Tests that can only be run under Python 3
7913 · datetime: This tests the subset of Hypothesis that depends on pytz
7915 · fakefactory: This tests the subset of Hypothesis that depends on
7917 fakefactory.
7918 · django: This tests the subset of Hypothesis that depends on django
7920 An example invocation for running the coverage subset of these tests:
7922 pip install -e .
7924 pip install pytest # you will probably want to use your own packaging here
7925 python -m pytest tests/cover
7926 Examples
7928 · arch linux
7929 · fedora
7931 · gentoo
7933
7935 One of the things that is often concerning for people using randomized
7936 testing like Hypothesis is the question of how to reproduce failing
7937 test cases.
7938 Fortunately Hypothesis has a number of features in support of this. The
7940 one you will use most commonly when developing locally is the example
7941 database, which means that you shouldn't have to think about the prob‐
7942 lem at all for local use - test failures will just automatically repro‐
7943 duce without you having to do anything.
7944 The example database is perfectly suitable for sharing between
7946 machines, but there currently aren't very good work flows for that, so
7947 Hypothesis provides a number of ways to make examples reproducible by
7948 adding them to the source code of your tests. This is particularly use‐
7949 ful when e.g. you are trying to run an example that has failed on your
7950 CI, or otherwise share them between machines.
7951 Providing explicit examples
7953 You can explicitly ask Hypothesis to try a particular example, using
7954 hypothesis.example(*args, **kwargs)
7956 A decorator which ensures a specific example is always tested.
7957 Hypothesis will run all examples you've asked for first. If any of them
7959 fail it will not go on to look for more examples.
7960 It doesn't matter whether you put the example decorator before or after
7962 given. Any permutation of the decorators in the above will do the same
7963 thing.
7964 Note that examples can be positional or keyword based. If they're posi‐
7966 tional then they will be filled in from the right when calling, so
7967 either of the following styles will work as expected:
7968 @given(text())
7970 @example("Hello world")
7971 @example(x="Some very long string")
7972 def test_some_code(x):
7973 assert True
7974 from unittest import TestCase
7976 class TestThings(TestCase):
7978 @given(text())
7979 @example("Hello world")
7980 @example(x="Some very long string")
7981 def test_some_code(self, x):
7982 assert True
7983 As with @given, it is not permitted for a single example to be a mix of
7985 positional and keyword arguments. Either are fine, and you can use one
7986 in one example and the other in another example if for some reason you
7987 really want to, but a single example must be consistent.
7988 Reproducing a test run with @seed
7990 hypothesis.seed(seed)
7991 seed: Start the test execution from a specific seed.
7992 May be any hashable object. No exact meaning for seed is pro‐
7994 vided other than that for a fixed seed value Hypothesis will try
7995 the same actions (insofar as it can given external sources of
7996 non- determinism. e.g. timing and hash randomization).
7997 Overrides the derandomize setting, which is designed to enable
7999 deterministic builds rather than reproducing observed failures.
8000 When a test fails unexpectedly, usually due to a health check failure,
8002 Hypothesis will print out a seed that led to that failure, if the test
8003 is not already running with a fixed seed. You can then recreate that
8004 failure using either the @seed decorator or (if you are running pytest)
8005 with --hypothesis-seed.
8006 Reproducing an example with with @reproduce_failure
8008 Hypothesis has an opaque binary representation that it uses for all
8009 examples it generates. This representation is not intended to be stable
8010 across versions or with respect to changes in the test, but can be used
8011 to to reproduce failures with the @reproduce_example decorator.
8012 hypothesis.reproduce_failure(version, blob)
8014 Run the example that corresponds to this data blob in order to
8015 reproduce a failure.
8016 A test with this decorator always runs only one example and
8018 always fails. If the provided example does not cause a failure,
8019 or is in some way invalid for this test, then this will fail
8020 with a DidNotReproduce error.
8021 This decorator is not intended to be a permanent addition to
8023 your test suite. It's simply some code you can add to ease
8024 reproduction of a problem in the event that you don't have
8025 access to the test database. Because of this, no compatibility
8026 guarantees are made between different versions of Hypothesis -
8027 its API may change arbitrarily from version to version.
8028 The intent is that you should never write this decorator by hand, but
8030 it is instead provided by Hypothesis. When a test fails with a falsi‐
8031 fying example, Hypothesis may print out a suggestion to use @repro‐
8032 duce_failure on the test to recreate the problem as follows:
8033 >>> from hypothesis import settings, given, PrintSettings
8035 >>> import hypothesis.strategies as st
8036 >>> @given(st.floats())
8037 ... @settings(print_blob=PrintSettings.ALWAYS)
8038 ... def test(f):
8039 ... assert f == f
8040 ...
8041 >>> try:
8042 ... test()
8043 ... except AssertionError:
8044 ... pass
8045 Falsifying example: test(f=nan)
8046 You can reproduce this example by temporarily adding @reproduce_failure(..., b'AAAA//AAAAAAAAEA') as a decorator on your test case
8048 Adding the suggested decorator to the test should reproduce the failure
8050 (as long as everything else is the same - changing the versions of
8051 Python or anything else involved, might of course affect the behaviour
8052 of the test! Note that changing the version of Hypothesis will result
8053 in a different error - each @reproduce_failure invocation is specific
8054 to a Hypothesis version).
8055 When to do this is controlled by the print_blob setting, which may be
8057 one of the following values:
8058 class hypothesis.PrintSettings
8060 Flags to determine whether or not to print a detailed example
8061 blob to use with reproduce_failure() for failing test cases.
8062 NEVER = 0
8064 Never print a blob.
8065 INFER = 1
8067 Make an educated guess as to whether it would be appro‐
8068 priate to print the blob.
8069 The current rules are that this will print if both:
8071 1. The output from Hypothesis appears to be unsuitable
8073 for use with example().
8074 2. The output is not too long.
8076 ALWAYS = 2
8078 Always print a blob on failure.
8079
8081 David R. MacIver
8082
8084 2013-2018, David R. MacIver
80853.66.11 Jul 28, 2018 HYPOTHESIS(1)
