Hypothesis tries to have good defaults for its behaviour, but sometimes that’s not enough and you need to tweak it.
@given invocation is as follows:
from hypothesis import given, settings @given(integers()) @settings(max_examples=500) def test_this_thoroughly(x): pass
This uses a
settings object which causes the test to receive a much larger
set of examples than normal.
This may be applied either before or after the given and the results are the same. The following is exactly equivalent:
from hypothesis import given, settings @settings(max_examples=500) @given(integers()) def test_this_thoroughly(x): pass
settings(parent=None, *, max_examples=not_set, derandomize=not_set, database=not_set, verbosity=not_set, phases=not_set, stateful_step_count=not_set, report_multiple_bugs=not_set, suppress_health_check=not_set, deadline=not_set, print_blob=not_set)¶
A settings object controls a variety of parameters that are used in falsification. These may control both the falsification strategy and the details of the data that is generated.
Default values are picked up from the settings.default object and changes made there will be picked up in newly created settings.
An instance of
ExampleDatabasethat will be used to save examples to and load previous examples from. May be
Nonein which case no storage will be used.
See the example database documentation for a list of built-in example database implementations, and how to define custom implementations.
If set, a duration (as timedelta, or integer or float number of milliseconds) that each individual example (i.e. each time your test function is called, not the whole decorated test) within a test is not allowed to exceed. Tests which take longer than that may be converted into errors (but will not necessarily be if close to the deadline, to allow some variability in test run time).
Set this to
Noneto disable this behaviour entirely.
If True, seed Hypothesis’ random number generator using a hash of the test function, so that every run will test the same set of examples until you update Hypothesis, Python, or the test function.
This allows you to check for regressions and look for bugs using separate settings profiles - for example running quick deterministic tests on every commit, and a longer non-deterministic nightly testing run.
Once this many satisfying examples have been considered without finding any counter-example, falsification will terminate.
The default value is chosen to suit a workflow where the test will be part of a suite that is regularly executed locally or on a CI server, balancing total running time against the chance of missing a bug.
If you are writing one-off tests, running tens of thousands of examples is quite reasonable as Hypothesis may miss uncommon bugs with default settings. For very complex code, we have observed Hypothesis finding novel bugs after several million examples while testing SymPy. If you are running more than 100k examples for a test, consider using our integration for coverage-guided fuzzing - it really shines when given minutes or hours to run.
Control which phases should be run. See the full documentation for more details
(Phase.explicit, Phase.reuse, Phase.generate, Phase.target, Phase.shrink)
If set to
True, Hypothesis will print code for failing examples that can be used with
@reproduce_failureto reproduce the failing example. The default is
TF_BUILDenv vars are set,
Because Hypothesis runs the test many times, it can sometimes find multiple bugs in a single run. Reporting all of them at once is usually very useful, but replacing the exceptions can occasionally clash with debuggers. If disabled, only the exception with the smallest minimal example is raised.
Number of steps to run a stateful program for before giving up on it breaking.
Control the verbosity level of Hypothesis messages
Controlling what runs¶
Hypothesis divides tests into five logically distinct phases:
Running explicit examples provided with the @example decorator.
Rerunning a selection of previously failing examples to reproduce a previously seen error
Generating new examples.
Mutating examples for targeted property-based testing.
Attempting to shrink an example found in previous phases (other than phase 1 - explicit examples cannot be shrunk). This turns potentially large and complicated examples which may be hard to read into smaller and simpler ones.
The phases setting provides you with fine grained control over which of these run,
with each phase corresponding to a value on the
Phase.explicitcontrols whether explicit examples are run.
Phase.reusecontrols whether previous examples will be reused.
Phase.generatecontrols whether new examples will be generated.
Phase.targetcontrols whether examples will be mutated for targeting.
Phase.shrinkcontrols whether examples will be shrunk.
The phases argument accepts a collection with any subset of these. e.g.
settings(phases=[Phase.generate, Phase.shrink]) will generate new examples
and shrink them, but will not run explicit examples or reuse previous failures,
settings(phases=[Phase.explicit]) will only run the explicit
Seeing intermediate result¶
To see what’s going on while Hypothesis runs your tests, you can turn up the verbosity setting.
>>> from hypothesis import find, settings, Verbosity >>> from hypothesis.strategies import lists, integers >>> @given(lists(integers())) ... @settings(verbosity=Verbosity.verbose) ... def f(x): assert not any(x) ... f() Trying example:  Falsifying example: [-1198601713, -67, 116, -29578] Shrunk example to [-1198601713] Shrunk example to [-1198601600] Shrunk example to [-1191228800] Shrunk example to [-8421504] Shrunk example to [-32896] Shrunk example to [-128] Shrunk example to  Shrunk example to  Shrunk example to  Shrunk example to  Shrunk example to  Shrunk example to  Shrunk example to  Shrunk example to  
The four levels are quiet, normal, verbose and debug. normal is the default, while in quiet mode Hypothesis will not print anything out, not even the final falsifying example. debug is basically verbose but a bit more so. You probably don’t want it.
Building settings objects¶
Settings can be created by calling
settings with any of the available settings
values. Any absent ones will be set to defaults:
>>> from hypothesis import settings >>> settings().max_examples 100 >>> settings(max_examples=10).max_examples 10
You can also pass a ‘parent’ settings object as the first argument, and any settings you do not specify as keyword arguments will be copied from the parent settings:
>>> parent = settings(max_examples=10) >>> child = settings(parent, deadline=None) >>> parent.max_examples == child.max_examples == 10 True >>> parent.deadline 200 >>> child.deadline is None True
At any given point in your program there is a current default settings,
settings.default. As well as being a settings object in its own
right, all newly created settings objects which are not explicitly based off
another settings are based off the default, so will inherit any values that are
not explicitly set from it.
You can change the defaults by using profiles.
Depending on your environment you may want different default settings. For example: during development you may want to lower the number of examples to speed up the tests. However, in a CI environment you may want more examples so you are more likely to find bugs.
Hypothesis allows you to define different settings profiles. These profiles can be loaded at any time.
register_profile(name, parent=None, **kwargs)¶
Registers a collection of values to be used as a settings profile.
Settings profiles can be loaded by name - for example, you might create a ‘fast’ profile which runs fewer examples, keep the ‘default’ profile, and create a ‘ci’ profile that increases the number of examples and uses a different database to store failures.
The arguments to this method are exactly as for
parentsettings, and keyword arguments for each setting that will be set differently to parent (or settings.default, if parent is None).
Return the profile with the given name.
Loads in the settings defined in the profile provided.
If the profile does not exist, InvalidArgument will be raised. Any setting not defined in the profile will be the library defined default for that setting.
Loading a profile changes the default settings but will not change the behaviour of tests that explicitly change the settings.
>>> from hypothesis import settings >>> settings.register_profile("ci", max_examples=1000) >>> settings().max_examples 100 >>> settings.load_profile("ci") >>> settings().max_examples 1000
Instead of loading the profile and overriding the defaults you can retrieve profiles for specific tests.
>>> settings.get_profile("ci").max_examples 1000
Optionally, you may define the environment variable to load a profile for you. This is the suggested pattern for running your tests on CI. The code below should run in a conftest.py or any setup/initialization section of your test suite. If this variable is not defined the Hypothesis defined defaults will be loaded.
>>> import os >>> from hypothesis import settings, Verbosity >>> settings.register_profile("ci", max_examples=1000) >>> settings.register_profile("dev", max_examples=10) >>> settings.register_profile("debug", max_examples=10, verbosity=Verbosity.verbose) >>> settings.load_profile(os.getenv(u'HYPOTHESIS_PROFILE', 'default'))
If you are using the hypothesis pytest plugin and your profiles are registered
by your conftest you can load one with the command line option
$ pytest tests --hypothesis-profile <profile-name>