Compatibility

Hypothesis does its level best to be compatible with everything you could possibly need it to be compatible with. Generally you should just try it and expect it to work. If it doesn’t, you can be surprised and check this document for the details.

Hypothesis versions

Backwards compatibility is better than backporting fixes, so we use semantic versioning and only support the most recent version of Hypothesis.

Documented APIs will not break except between major version bumps. All APIs mentioned in this documentation are public unless explicitly noted as provisional, in which case they may be changed in minor releases. Undocumented attributes, modules, and behaviour may include breaking changes in patch releases.

Deprecations

Deprecated features will emit warnings for at least six months, and then be removed in the following major release.

Note however that not all warnings are subject to this grace period; sometimes we strengthen validation by adding a warning and these may become errors immediately at a major release.

We use custom exception and warning types, so you can see exactly where an error came from, or turn only our warnings into errors.

class hypothesis.errors.HypothesisDeprecationWarning

A deprecation warning issued by Hypothesis.

Actually inherits from FutureWarning, because DeprecationWarning is hidden by the default warnings filter.

You can configure the Python python:warnings to handle these warnings differently to others, either turning them into errors or suppressing them entirely. Obviously we would prefer the former!

Python versions

Hypothesis is supported and tested on CPython 3.9+, i.e. all versions of CPython with upstream support, along with PyPy for the same versions. 32-bit builds of CPython also work, though we only test them on Windows.

In general Hypothesis does not officially support anything except the latest patch release of any version of Python it supports. Earlier releases should work and bugs in them will get fixed if reported, but they’re not tested in CI and no guarantees are made.

Operating systems

In theory Hypothesis should work anywhere that Python does. In practice it is only known to work and regularly tested on OS X, Windows and Linux, and you may experience issues running it elsewhere.

If you’re using something else and it doesn’t work, do get in touch and I’ll try to help, but unless you can come up with a way for me to run a CI server on that operating system it probably won’t stay fixed due to the inevitable march of time.

Testing frameworks

In general Hypothesis goes to quite a lot of effort to generate things that look like normal Python test functions that behave as closely to the originals as possible, so it should work sensibly out of the box with every test framework.

If your testing relies on doing something other than calling a function and seeing if it raises an exception then it probably won’t work out of the box. In particular things like tests which return generators and expect you to do something with them (e.g. nose’s yield based tests) will not work. Use a decorator or similar to wrap the test to take this form, or ask the framework maintainer to support our hooks for inserting such a wrapper later.

In terms of what’s actually known to work:

• Hypothesis integrates as smoothly with pytest and unittest as we can make it, and this is verified as part of the CI.

• pytest fixtures work in the usual way for tests that have been decorated with @given - just avoid passing a strategy for each argument that will be supplied by a fixture. However, function-scoped fixtures will run only once for the whole function, not per example. To proactively warn you about this case, we raise HealthCheck.function_scoped_fixture, unless suppressed with settings.suppress_health_check.

• The python:unittest.mock.patch() decorator works with @given, but we recommend using it as a context manager within the decorated test to ensure that the mock is per-test-case and avoid poor interactions with Pytest fixtures.

• Nose works fine with Hypothesis, and this is tested as part of the CI. yield based tests simply won’t work.

• Integration with Django’s testing requires use of the Django extra. The issue is that in Django’s tests’ normal mode of execution it will reset the database once per test rather than once per example, which is not what you want.

• coverage works out of the box with Hypothesis; our own test suite has 100% branch coverage.

Optional packages

The supported versions of optional packages, for strategies in hypothesis.extra, are listed in the documentation for that extra. Our general goal is to support all versions that are supported upstream.

Thread-Safety Policy

As of version 6.136.9, Hypothesis is thread-safe. Each of the following is fully supported, and tested regularly in CI:

• Running tests in multiple processes

• Running separate tests in multiple threads

• Running the same test in multiple threads

If you find a bug here, please report it. The main risks internally are global state, shared caches, and cached strategies.

Thread usage inside tests

Tests that spawn threads internally are supported by Hypothesis.

However, these as with any Hypothesis test, these tests must have deterministic test outcomes and data generation. For example, if timing changes in the threads change the sequence of dynamic draws from @composite or data(), Hypothesis may report the test as flaky. The solution here is to refactor data generation so it does not depend on test timings.

Cross-thread API calls

In theory, Hypothesis supports cross-thread API calls, for instance spawning a thread inside of a test and using that to draw from @composite or data(), or to call event(), target(), or assume().

However, we have not explicitly audited this behavior, and do not regularly test it in our CI. If you find a bug here, please report it. If our investigation determines that we cannot support cross-thread calls for the feature in question, we will update this page accordingly.

Type hints

We ship type hints with Hypothesis itself. Though we always try to minimize breakage, we may make breaking changes to these between minor releases and do not commit to maintaining a fully stable interface for type hints.

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.

There are known issues with inferring the type of examples generated by deferred(), recursive(), one_of(), dictionaries(), and fixed_dictionaries(). We’re following proposed updates to Python’s typing standards, but unfortunately the long-standing interfaces of these strategies cannot (yet) be statically typechecked.