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")
With the 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:
>>> 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(f"Shuffle: {ls2!r}")
... 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 for the minimal failing example of the test in order to include any additional information you might need in your test.
Test statistics¶
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 --hypothesis-show-statistics
:
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 max_examples
.
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 event
function:
- hypothesis.event(value, payload='')[source]¶
Record an event that occurred during this test. Statistics on the number of test runs with each event will be reported at the end if you run Hypothesis in statistics reporting mode.
Event values should be strings or convertible to them. If an optional payload is given, it will be included in the string for Test statistics.
from hypothesis import event, given, strategies as st
@given(st.integers().filter(lambda x: x % 2 == 0))
def test_even_integers(i):
event(f"i mod 3 = {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
Arguments to 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 str
.
Making assumptions¶
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.
- hypothesis.assume(condition)[source]¶
Calling
assume
is 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:
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:
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:
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:
Unsurprisingly this fails and gives the falsifying example []
.
Adding assume(xs)
to this removes the trivial empty example and gives us [0]
.
Adding 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 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.
Defining strategies¶
The type of object that is used to explore the examples given to your test
function is called a SearchStrategy
.
These are created using the functions
exposed in the hypothesis.strategies
module.
Many of these strategies expose a variety of arguments you can use to customize
generation. For example for integers you can specify min
and 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¶
- hypothesis.given(*_given_arguments, **_given_kwargs)[source]¶
A decorator for turning a test function that accepts arguments into a randomized test.
This is the main entry point to Hypothesis.
The @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:
The rules for determining what are valid uses of given
are as follows:
You may pass any keyword argument to
given
.Positional arguments to
given
are 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
given
may not have any defaults.
The reason for the “rightmost named arguments” behaviour is so that
using @given
with instance methods works: self
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 @given
.
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, or your target metric might accidentally lead Hypothesis away from bugs - but if there is a natural metric like “floating-point error”, “load factor” or “queue length”, we encourage you to experiment with targeted testing.
- hypothesis.target(observation, *, label='')[source]¶
Calling this function with an
int
orfloat
observation 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 aserror
approaches zero.Example metrics:
Number of elements in a collection, or tasks in a queue
Mean or maximum runtime of a task (or both, if you use
label
)Compression ratio for data (perhaps per-algorithm or per-level)
Number of steps taken by a state machine
The optional
label
argument 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 calltarget()
with any label more than once per test case.Note
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.Test statistics include the best score seen for each label, which can help avoid the threshold problem when the minimal example shrinks right down to the threshold of failure (issue #2180).
from hypothesis import given, strategies as st, target
@given(st.floats(0, 1e100), st.floats(0, 1e100), st.floats(0, 1e100))
def test_associativity_with_target(a, b, c):
ab_c = (a + b) + c
a_bc = a + (b + c)
difference = abs(ab_c - a_bc)
target(difference) # Without this, the test almost always passes
assert difference < 2.0
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.
For example, 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:
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 execute_example
.
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:
An alternative hook is provided for use by test runner extensions such as
pytest-trio, which cannot use the execute_example
method.
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
.
For authors of test runners however, assigning to the inner_test
attribute
of the hypothesis
attribute of the test will replace the interior test.
Note
The new inner_test
must accept and pass through all the *args
and **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
and numpy.random
random number generators for you, and you can register others:
- hypothesis.register_random(r)[source]¶
Register (a weakref to) the given Random-like instance for management by Hypothesis.
You can pass instances of structural subtypes of
random.Random
(i.e., objects with seed, getstate, and setstate methods) toregister_random(r)
to have their states seeded and restored in the same way as the global PRNGs from therandom
andnumpy.random
modules.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, always seeding them to zero and restoring the previous state after the test, or, reproducibly varied if you choose to use the
random_module()
strategy.register_random
only makes weakrefs tor
, thusr
will only be managed by Hypothesis as long as it has active references elsewhere at runtime. The patternregister_random(MyRandom())
will raise aReferenceError
to help protect users from this issue. This check does not occur for the PyPy interpreter. See the following example for an illustration of this issuedef my_BROKEN_hook(): r = MyRandomLike() # `r` will be garbage collected after the hook resolved # and Hypothesis will 'forget' that it was registered register_random(r) # Hypothesis will emit a warning rng = MyRandomLike() def my_WORKING_hook(): register_random(rng)
Inferred strategies¶
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
target
(using signature()
).
If there are required arguments with type annotations and
no strategy was passed to builds()
,
from_type()
is used to fill them in.
You can also pass the value ...
(Ellipsis
) as a keyword
argument, to force this inference for arguments with a default value.
- hypothesis.infer¶
@given
does not perform any implicit inference
for required arguments, as this would break compatibility with pytest fixtures.
...
(Ellipsis
), can be used as a keyword argument to explicitly fill
in an argument from its type annotation. You can also use the hypothesis.infer
alias if writing a literal ...
seems too weird.
@given(...)
can also be specified to fill all arguments from their type annotations.
Limitations¶
Hypothesis does not inspect PEP 484 type comments at runtime. While
from_type()
will work as usual, inference in
builds()
and @given
will only work if you manually create the __annotations__
attribute
(e.g. by using @annotations(...)
and @returns(...)
decorators).
The typing
module changes between different Python releases,
including at minor versions. These
are all supported on a best-effort basis,
but you may encounter problems. Please report them to us, and consider
updating to a newer version of Python as a workaround.
Type annotations in Hypothesis¶
If you install Hypothesis and use mypy 0.590+, or another PEP 561-compatible tool, the type checker should automatically pick up our type hints.
Note
Hypothesis’ type hints may make breaking changes between minor releases.
Upstream tools and conventions about type hints remain in flux - for
example the typing
module itself is provisional - 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
deferred()
, recursive()
,
one_of()
, dictionaries()
,
and fixed_dictionaries()
.
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]: ...
- class hypothesis.strategies.SearchStrategy¶
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 typeSearchStrategy[int]
.lists(integers())
is of typeSearchStrategy[List[int]]
.SearchStrategy[Dog]
is a subtype ofSearchStrategy[Animal]
ifDog
is a subtype ofAnimal
(as seems likely).
Warning
SearchStrategy
should only be used
in type hints. Please do not inherit from, compare to, or otherwise
use it in any way outside of type hints. The only supported way to
construct objects of this type is to use the functions provided by the
hypothesis.strategies
module!
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-statistics
can be used to display test and data generation statistics.pytest --hypothesis-profile=<profile name>
can be used to load a settings profile.pytest --hypothesis-verbosity=<level name>
can be used to override the current verbosity level.pytest --hypothesis-seed=<an int>
can be used to reproduce a failure with a particular seed.pytest --hypothesis-explain
can be used to temporarily enable the explain phase.
Finally, all tests that are defined with Hypothesis automatically have
@pytest.mark.hypothesis
applied to them. See here for information
on working with markers.
Note
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¶
Tip
Sometimes, you might want to point a traditional fuzzer such as
python-afl, pythonfuzz,
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?
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,fuzz_one_input
returnsNone
.If the bytestring was valid and the test passed,
fuzz_one_input
returns 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_input
will 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
@reproduce_failure
decorator.
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.
The
database
setting 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 response to the ‘fuzzer taming’ problem.The
verbosity
andstateful_step_count
settings work as usual.
The deadline
, derandomize
, max_examples
, phases
, print_blob
,
report_multiple_bugs
, and suppress_health_check
settings do not affect
fuzzing mode.
Thread-Safety Policy¶
As discussed in issue #2719, Hypothesis is not truly thread-safe and that’s unlikely to change in the future. This policy therefore describes what you can expect if you use Hypothesis with multiple threads.
Running tests in multiple processes, e.g. with pytest -n auto
, is fully
supported and we test this regularly in CI - thanks to process isolation, we only
need to ensure that DirectoryBasedExampleDatabase
can’t tread on its own toes too badly. If you find a bug here we will fix it ASAP.
Running separate tests in multiple threads is not something we design or test for, and is not formally supported. That said, anecdotally it does mostly work and we would like it to keep working - we accept reasonable patches and low-priority bug reports. The main risks here are global state, shared caches, and cached strategies.
Using multiple threads within a single test , or running a single test simultaneously in multiple threads, makes it pretty easy to trigger internal errors. We usually accept patches for such issues unless readability or single-thread performance suffer.
Hypothesis assumes that tests are single-threaded, or do a sufficiently-good job of
pretending to be single-threaded. Tests that use helper threads internally should be OK,
but the user must be careful to ensure that test outcomes are still deterministic.
In particular it counts as nondeterministic if helper-thread timing changes the sequence
of dynamic draws using e.g. the data()
.
Interacting with any Hypothesis APIs from helper threads might do weird/bad things,
so avoid that too - we rely on thread-local variables in a few places, and haven’t
explicitly tested/audited how they respond to cross-thread API calls. While
data()
and equivalents are the most obvious danger,
other APIs might also be subtly affected.