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.
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.
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
warningsto handle these warnings differently to others, either turning them into errors or suppressing them entirely. Obviously we would prefer the former!
Hypothesis is supported and tested on CPython 3.8+, 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.
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.
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, each fixture will run once for the whole function, not once per example. Decorating a fixture function with
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.
yieldbased tests simply won’t work.
Integration with Django’s testing requires use of the Hypothesis for Django users 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.
The supported versions of optional packages, for strategies in
are listed in the documentation for that extra. Our general goal is to support
all versions that are supported upstream.
Regularly verifying this¶
Everything mentioned above as explicitly supported is checked on every commit with GitHub Actions. Our continuous delivery pipeline runs all of these checks before publishing each release, so when we say they’re supported we really mean it.