Details and advanced features¶
This is an account of slightly less common Hypothesis features that you don’t need to get started but will nevertheless make your life easier.
Additional test output¶
Normally the output of a failing test will look something like:
Falsifying example: test_a_thing(x=1, y="foo")
repr of each keyword argument being printed.
Sometimes this isn’t enough, either because you have a value with a
__repr__() method that isn’t very descriptive or because you need to see the output of some
intermediate steps of your test. That’s where the
note function comes in:
Report this value in the final execution.
>>> from hypothesis import given, note, strategies as st >>> @given(st.lists(st.integers()), st.randoms()) ... def test_shuffle_is_noop(ls, r): ... ls2 = list(ls) ... r.shuffle(ls2) ... note("Shuffle: %r" % (ls2)) ... assert ls == ls2 ... >>> try: ... test_shuffle_is_noop() ... except AssertionError: ... print('ls != ls2') Falsifying example: test_shuffle_is_noop(ls=[0, 1], r=RandomWithSeed(1)) Shuffle: [1, 0] ls != ls2
The note is printed in the final run of the test in order to include any additional information you might need in your test.
If you are using pytest you can see a number of statistics about the executed tests
by passing the command line argument
--hypothesis-show-statistics. This will include
some general statistics about the test:
For example if you ran the following with
from hypothesis import given, strategies as st @given(st.integers()) def test_integers(i): pass
You would see:
- during generate phase (0.06 seconds): - Typical runtimes: < 1ms, ~ 47% in data generation - 100 passing examples, 0 failing examples, 0 invalid examples - Stopped because settings.max_examples=100
The final “Stopped because” line is particularly important to note: It tells you the
setting value that determined when the test should stop trying new examples. This
can be useful for understanding the behaviour of your tests. Ideally you’d always want
this to be
In some cases (such as filtered and recursive strategies) you will see events mentioned which describe some aspect of the data generation:
from hypothesis import given, strategies as st @given(st.integers().filter(lambda x: x % 2 == 0)) def test_even_integers(i): pass
You would see something like:
test_even_integers: - during generate phase (0.08 seconds): - Typical runtimes: < 1ms, ~ 57% in data generation - 100 passing examples, 0 failing examples, 12 invalid examples - Events: * 51.79%, Retried draw from integers().filter(lambda x: x % 2 == 0) to satisfy filter * 10.71%, Aborted test because unable to satisfy integers().filter(lambda x: x % 2 == 0) - Stopped because settings.max_examples=100
You can also mark custom events in a test using the
Record an event that occurred this test. Statistics on number of test runs with each event will be reported at the end if you run Hypothesis in statistics reporting mode.
Events should be strings or convertible to them.
from hypothesis import given, event, strategies as st @given(st.integers().filter(lambda x: x % 2 == 0)) def test_even_integers(i): event("i mod 3 = %d" % (i % 3,))
You will then see output like:
test_even_integers: - during generate phase (0.09 seconds): - Typical runtimes: < 1ms, ~ 59% in data generation - 100 passing examples, 0 failing examples, 32 invalid examples - Events: * 54.55%, Retried draw from integers().filter(lambda x: x % 2 == 0) to satisfy filter * 31.06%, i mod 3 = 2 * 28.79%, i mod 3 = 0 * 24.24%, Aborted test because unable to satisfy integers().filter(lambda x: x % 2 == 0) * 15.91%, i mod 3 = 1 - Stopped because settings.max_examples=100
event can be any hashable type, but two events will be considered the same
if they are the same when converted to a string with
Sometimes Hypothesis doesn’t give you exactly the right sort of data you want - it’s mostly of the right shape, but some examples won’t work and you don’t want to care about them. You can just ignore these by aborting the test early, but this runs the risk of accidentally testing a lot less than you think you are. Also it would be nice to spend less time on bad examples - if you’re running 100 examples per test (the default) and it turns out 70 of those examples don’t match your needs, that’s a lot of wasted time.
assumeis like an assert that marks the example as bad, rather than failing the test.
This allows you to specify properties that you assume will be true, and let Hypothesis try to avoid similar examples in future.
For example suppose you had the following test:
@given(floats()) def test_negation_is_self_inverse(x): assert x == -(-x)
Running this gives us:
Falsifying example: test_negation_is_self_inverse(x=float('nan')) AssertionError
This is annoying. We know about NaN and don’t really care about it, but as soon as Hypothesis finds a NaN example it will get distracted by that and tell us about it. Also the test will fail and we want it to pass.
So let’s block off this particular example:
from math import isnan @given(floats()) def test_negation_is_self_inverse_for_non_nan(x): assume(not isnan(x)) assert x == -(-x)
And this passes without a problem.
In order to avoid the easy trap where you assume a lot more than you intended, Hypothesis will fail a test when it can’t find enough examples passing the assumption.
If we’d written:
@given(floats()) def test_negation_is_self_inverse_for_non_nan(x): assume(False) assert x == -(-x)
Then on running we’d have got the exception:
Unsatisfiable: Unable to satisfy assumptions of hypothesis test_negation_is_self_inverse_for_non_nan. Only 0 examples considered satisfied assumptions
How good is assume?¶
Hypothesis has an adaptive exploration strategy to try to avoid things which falsify assumptions, which should generally result in it still being able to find examples in hard to find situations.
Suppose we had the following:
@given(lists(integers())) def test_sum_is_positive(xs): assert sum(xs) > 0
Unsurprisingly this fails and gives the falsifying example
assume(xs) to this removes the trivial empty example and gives us
assume(all(x > 0 for x in xs)) and it passes: the sum of a list of
positive integers is positive.
The reason that this should be surprising is not that it doesn’t find a counter-example, but that it finds enough examples at all.
In order to make sure something interesting is happening, suppose we wanted to
try this for long lists. e.g. suppose we added an
assume(len(xs) > 10) to it.
This should basically never find an example: a naive strategy would find fewer
than one in a thousand examples, because if each element of the list is
negative with probability one-half, you’d have to have ten of these go the right
way by chance. In the default configuration Hypothesis gives up long before
it’s tried 1000 examples (by default it tries 200).
Here’s what happens if we try to run this:
@given(lists(integers())) def test_sum_is_positive(xs): assume(len(xs) > 1) assume(all(x > 0 for x in xs)) print(xs) assert sum(xs) > 0
[17, 12, 7, 13, 11, 3, 6, 9, 8, 11, 47, 27, 1, 31, 1] [6, 2, 29, 30, 25, 34, 19, 15, 50, 16, 10, 3, 16] [25, 17, 9, 19, 15, 2, 2, 4, 22, 10, 10, 27, 3, 1, 14, 17, 13, 8, 16, 9, 2, ...] [17, 65, 78, 1, 8, 29, 2, 79, 28, 18, 39] [13, 26, 8, 3, 4, 76, 6, 14, 20, 27, 21, 32, 14, 42, 9, 24, 33, 9, 5, 15, ...] [2, 1, 2, 2, 3, 10, 12, 11, 21, 11, 1, 16]
As you can see, Hypothesis doesn’t find many examples here, but it finds some - enough to keep it happy.
In general if you can shape your strategies better to your tests you should - for example
integers(1, 1000) is a lot better than
assume(1 <= x <= 1000), but
assume will take you a long way if you can’t.
Many of these strategies expose a variety of arguments you can use to customize
generation. For example for integers you can specify
max values of
integers you want.
If you want to see exactly what a strategy produces you can ask for an example:
>>> integers(min_value=0, max_value=10).example() 1
Many strategies are built out of other strategies. For example, if you want to define a tuple you need to say what goes in each element:
>>> from hypothesis.strategies import tuples >>> tuples(integers(), integers()).example() (-24597, 12566)
Further details are available in a separate document.
The gory details of given parameters¶
A decorator for turning a test function that accepts arguments into a randomized test.
This is the main entry point to Hypothesis.
@given decorator may be used to specify
which arguments of a function should be parametrized over. You can use
either positional or keyword arguments, but not a mixture of both.
For example all of the following are valid uses:
@given(integers(), integers()) def a(x, y): pass @given(integers()) def b(x, y): pass @given(y=integers()) def c(x, y): pass @given(x=integers()) def d(x, y): pass @given(x=integers(), y=integers()) def e(x, **kwargs): pass @given(x=integers(), y=integers()) def f(x, *args, **kwargs): pass class SomeTest(TestCase): @given(integers()) def test_a_thing(self, x): pass
The following are not:
@given(integers(), integers(), integers()) def g(x, y): pass @given(integers()) def h(x, *args): pass @given(integers(), x=integers()) def i(x, y): pass @given() def j(x, y): pass
The rules for determining what are valid uses of
given are as follows:
You may pass any keyword argument to
Positional arguments to
givenare equivalent to the rightmost named arguments for the test function.
Positional arguments may not be used if the underlying test function has varargs, arbitrary keywords, or keyword-only arguments.
Functions tested with
givenmay not have any defaults.
The reason for the “rightmost named arguments” behaviour is so that
@given with instance methods works:
will be passed to the function as normal and not be parametrized over.
The function returned by given has all the same arguments as the original
test, minus those that are filled in by
Check the notes on framework compatibility
to see how this affects other testing libraries you may be using.
Targeted example generation¶
Targeted property-based testing combines the advantages of both search-based and property-based testing. Instead of being completely random, T-PBT uses a search-based component to guide the input generation towards values that have a higher probability of falsifying a property. This explores the input space more effectively and requires fewer tests to find a bug or achieve a high confidence in the system being tested than random PBT. (Löscher and Sagonas)
This is not always a good idea - for example calculating the search metric might take time better spent running more uniformly-random test cases - but Hypothesis has experimental support for targeted PBT you may wish to try.
target(observation, *, label='')¶
Calling this function with an
floatobservation gives it feedback with which to guide our search for inputs that will cause an error, in addition to all the usual heuristics. Observations must always be finite.
Hypothesis will try to maximize the observed value over several examples; almost any metric will work so long as it makes sense to increase it. For example,
-abs(error)is a metric that increases as
Number of elements in a collection, or tasks in a queue
Mean or maximum runtime of a task (or both, if you use
Compression ratio for data (perhaps per-algorithm or per-level)
Number of steps taken by a state machine
labelargument can be used to distinguish between and therefore separately optimise distinct observations, such as the mean and standard deviation of a dataset. It is an error to call
target()with any label more than once per test case.
The more examples you run, the better this technique works.
As a rule of thumb, the targeting effect is noticeable above
max_examples=1000, and immediately obvious by around ten thousand examples per label used by your test.
hypothesis.targetis considered experimental, and may be radically changed or even removed in a future version. If you find it useful, please let us know so we can share and build on that success!
We recommend that users also skim the papers introducing targeted PBT; from ISSTA 2017 and ICST 2018. For the curious, the initial implementation in Hypothesis uses hill-climbing search via a mutating fuzzer, with some tactics inspired by simulated annealing to avoid getting stuck and endlessly mutating a local maximum.
Custom function execution¶
Hypothesis provides you with a hook that lets you control how it runs examples.
This lets you do things like set up and tear down around each example, run
examples in a subprocess, transform coroutine tests into normal tests, etc.
TransactionTestCase in the
Django extra runs each example in a separate database transaction.
The way this works is by introducing the concept of an executor. An executor is essentially a function that takes a block of code and run it. The default executor is:
def default_executor(function): return function()
You define executors by defining a method
execute_example on a class. Any
test methods on that class with
@given used on them will use
self.execute_example as an executor with which to run tests. For example,
the following executor runs all its code twice:
from unittest import TestCase class TestTryReallyHard(TestCase): @given(integers()) def test_something(self, i): perform_some_unreliable_operation(i) def execute_example(self, f): f() return f()
Note: The functions you use in map, etc. will run inside the executor. i.e.
they will not be called until you invoke the function passed to
An executor must be able to handle being passed a function which returns None, otherwise it won’t be able to run normal test cases. So for example the following executor is invalid:
from unittest import TestCase class TestRunTwice(TestCase): def execute_example(self, f): return f()()
and should be rewritten as:
from unittest import TestCase class TestRunTwice(TestCase): def execute_example(self, f): result = f() if callable(result): result = result() return result
An alternative hook is provided for use by test runner extensions such as
pytest-trio, which cannot use the
This is not recommended for end-users - it is better to write a complete
test function directly, perhaps by using a decorator to perform the same
transformation before applying
@given(x=integers()) @pytest.mark.trio async def test(x): ... # Illustrative code, inside the pytest-trio plugin test.hypothesis.inner_test = lambda x: trio.run(test, x)
For authors of test runners however, assigning to the
hypothesis attribute of the test will replace the interior test.
inner_test must accept and pass through all the
**kwargs expected by the original test.
If the end user has also specified a custom executor using the
execute_example method, it - and all other execution-time logic - will
be applied to the new inner test assigned by the test runner.
Making random code deterministic¶
While Hypothesis’ example generation can be used for nondeterministic tests, debugging anything nondeterministic is usually a very frustrating exercise. To make things worse, our example shrinking relies on the same input causing the same failure each time - though we show the un-shrunk failure and a decent error message if it doesn’t.
By default, Hypothesis will handle the global
random number generators for you, and you can register others:
Register the given Random instance for management by Hypothesis.
You can pass
random.Randominstances (or other objects with seed, getstate, and setstate methods) to
register_random(r)to have their states seeded and restored in the same way as the global PRNGs from the
All global PRNGs, from e.g. simulation or scheduling frameworks, should be registered to prevent flaky tests. Hypothesis will ensure that the PRNG state is consistent for all test runs, or reproducibly varied if you choose to use the
In some cases, Hypothesis can work out what to do when you omit arguments. This is based on introspection, not magic, and therefore has well-defined limits.
builds() will check the signature of the
If there are required arguments with type annotations and
no strategy was passed to
from_type() is used to fill them in.
You can also pass the special value
hypothesis.infer as a keyword
argument, to force this inference for arguments with a default value.
>>> def func(a: int, b: str): ... return [a, b] >>> builds(func).example() [-6993, '']
@given does not perform any implicit inference
for required arguments, as this would break compatibility with pytest fixtures.
infer can be used as a keyword argument to explicitly
fill in an argument from its type annotation.
@given(a=infer) def test(a: int): pass # is equivalent to @given(a=integers()) def test(a): pass
Hypothesis does not inspect PEP 484 type comments at runtime. While
from_type() will work as usual, inference in
will only work if you manually create the
(e.g. by using
typing module is provisional and has a number of internal
changes between Python 3.5.0 and 3.6.1, including at minor versions. These
are all supported on a best-effort basis, but you may encounter problems with
an old version of the module. Please report them to us, and consider
updating to a newer version of Python as a workaround.
Type annotations in Hypothesis¶
Hypothesis’ type hints may make breaking changes between minor releases.
Upstream tools and conventions about type hints remain in flux - for
typing module itself is provisional, and Mypy
has not yet reached version 1.0 - and we plan to support the latest
version of this ecosystem, as well as older versions where practical.
We may also find more precise ways to describe the type of various interfaces, or change their type and runtime behaviour together in a way which is otherwise backwards-compatible. We often omit type hints for deprecated features or arguments, as an additional form of warning.
There are known issues inferring the type of examples generated by
We will fix these, and require correspondingly newer versions of Mypy for type
hinting, as the ecosystem improves.
Writing downstream type hints¶
Projects that provide Hypothesis strategies and use type hints may wish to annotate their strategies too. This is a supported use-case, again on a best-effort provisional basis. For example:
def foo_strategy() -> SearchStrategy[Foo]: ...
SearchStrategy is the type of all strategy
objects. It is a generic type, and covariant in the type of the examples
it creates. For example:
integers()is of type
lists(integers())is of type
SearchStrategy[Dog]is a subtype of
Dogis a subtype of
Animal(as seems likely).
The Hypothesis pytest plugin¶
Hypothesis includes a tiny plugin to improve integration with pytest, which is activated by default (but does not affect other test runners). It aims to improve the integration between Hypothesis and Pytest by providing extra information and convenient access to config options.
pytest --hypothesis-show-statisticscan be used to display test and data generation statistics.
pytest --hypothesis-seed=<an int>can be used to reproduce a failure with a particular seed.
Finally, all tests that are defined with Hypothesis automatically have
@pytest.mark.hypothesis applied to them. See here for information
on working with markers.
Pytest will load the plugin automatically if Hypothesis is installed. You don’t need to do anything at all to use it.
Use with external fuzzers¶
Sometimes, you might want to point a traditional fuzzer such as
or Google’s atheris (for Python and native extensions)
at your code. Wouldn’t it be nice if you could use any of your
@given tests as fuzz targets, instead of
converting bytestrings into your objects by hand?
@given(st.text()) def test_foo(s): ... # This is a traditional fuzz target - call it with a bytestring, # or a binary IO object, and it runs the test once. fuzz_target = test_foo.hypothesis.fuzz_one_input # For example: fuzz_target(b"\x00\x00\x00\x00\x00\x00\x00\x00") fuzz_target(io.BytesIO(...))
Depending on the input to
fuzz_one_input, one of three things will happen:
If the bytestring was invalid, for example because it was too short or failed a filter or
assume()too many times,
If the bytestring was valid and the test passed,
fuzz_one_inputreturns a canonicalised and pruned buffer which will replay that test case. This is provided as an option to improve the performance of mutating fuzzers, but can safely be ignored.
If the test failed, i.e. raised an exception,
fuzz_one_inputwill add the pruned buffer to the Hypothesis example database and then re-raise that exception. All you need to do to reproduce, minimize, and de-duplicate all the failures found via fuzzing is run your test suite!
Note that the interpretation of both input and output bytestrings is specific
to the exact version of Hypothesis you are using and the strategies given to
the test, just like the example database and
Interaction with settings¶
fuzz_one_input uses just enough of Hypothesis’ internals to drive your
test function with a fuzzer-provided bytestring, and most settings therefore
have no effect in this mode. We recommend running your tests the usual way
before fuzzing to get the benefits of healthchecks, as well as afterwards to
replay, shrink, deduplicate, and report whatever errors were discovered.
databasesetting is used by fuzzing mode - adding failures to the database to be replayed when you next run your tests is our preferred reporting mechanism and reponse to the ‘fuzzer taming’ problem.
suppress_health_check settings do not affect