Integrations Reference¶

Reference for Hypothesis features with a defined interface, but no code API.

Ghostwriter¶

Writing tests with Hypothesis frees you from the tedium of deciding on and writing out specific inputs to test. Now, the hypothesis.extra.ghostwriter module can write your test functions for you too!

The idea is to provide an easy way to start property-based testing, and a seamless transition to more complex test code - because ghostwritten tests are source code that you could have written for yourself.

So just pick a function you’d like tested, and feed it to one of the functions below. They follow imports, use but do not require type annotations, and generally do their best to write you a useful test. You can also use our command-line interface:

$ hypothesis write --help
Usage: hypothesis write [OPTIONS] FUNC...

  `hypothesis write` writes property-based tests for you!

  Type annotations are helpful but not required for our advanced
  introspection and templating logic.  Try running the examples below to see
  how it works:

      hypothesis write gzip
      hypothesis write numpy.matmul
      hypothesis write pandas.from_dummies
      hypothesis write re.compile --except re.error
      hypothesis write --equivalent ast.literal_eval eval
      hypothesis write --roundtrip json.dumps json.loads
      hypothesis write --style=unittest --idempotent sorted
      hypothesis write --binary-op operator.add

Options:
  --roundtrip                 start by testing write/read or encode/decode!
  --equivalent                very useful when optimising or refactoring code
  --errors-equivalent         --equivalent, but also allows consistent errors
  --idempotent                check that f(x) == f(f(x))
  --binary-op                 associativity, commutativity, identity element
  --style [pytest|unittest]   pytest-style function, or unittest-style method?
  -e, --except OBJ_NAME       dotted name of exception(s) to ignore
  --annotate / --no-annotate  force ghostwritten tests to be type-annotated
                              (or not).  By default, match the code to test.
  -h, --help                  Show this message and exit.

Tip

Using a light theme? Hypothesis respects NO_COLOR and DJANGO_COLORS=light.

Note

The ghostwriter requires black, but the generated code only requires Hypothesis itself.

Note

Legal questions? While the ghostwriter fragments and logic is under the MPL-2.0 license like the rest of Hypothesis, the output from the ghostwriter is made available under the Creative Commons Zero (CC0) public domain dedication, so you can use it without any restrictions.

hypothesis.extra.ghostwriter.magic( *modules_or_functions, except_=(), style='pytest', annotate=None, )[source]¶

Guess which ghostwriters to use, for a module or collection of functions.

As for all ghostwriters, the except_ argument should be an python:Exception or tuple of exceptions, and style may be either "pytest" to write test functions or "unittest" to write test methods and TestCase.

After finding the public functions attached to any modules, the magic ghostwriter looks for pairs of functions to pass to roundtrip(), then checks for binary_operation() and ufunc() functions, and any others are passed to fuzz().

For example, try hypothesis write gzip on the command line!

hypothesis.extra.ghostwriter.fuzz(func, *, except_=(), style='pytest', annotate=None)[source]¶

Write source code for a property-based test of func.

The resulting test checks that valid input only leads to expected exceptions. For example:

from re import compile, error

from hypothesis.extra import ghostwriter

ghostwriter.fuzz(compile, except_=error)

Gives:

# This test code was written by the `hypothesis.extra.ghostwriter` module
# and is provided under the Creative Commons Zero public domain dedication.
import re

from hypothesis import given, reject, strategies as st

# TODO: replace st.nothing() with an appropriate strategy


@given(pattern=st.nothing(), flags=st.just(0))
def test_fuzz_compile(pattern, flags):
    try:
        re.compile(pattern=pattern, flags=flags)
    except re.error:
        reject()

Note that it includes all the required imports. Because the pattern parameter doesn’t have annotations or a default argument, you’ll need to specify a strategy - for example text() or binary(). After that, you have a test!

hypothesis.extra.ghostwriter.idempotent( func, *, except_=(), style='pytest', annotate=None, )[source]¶

Write source code for a property-based test of func.

The resulting test checks that if you call func on it’s own output, the result does not change. For example:

from typing import Sequence

from hypothesis.extra import ghostwriter

def timsort(seq: Sequence[int]) -> Sequence[int]:
    return sorted(seq)

ghostwriter.idempotent(timsort)

Gives:

# This test code was written by the `hypothesis.extra.ghostwriter` module
# and is provided under the Creative Commons Zero public domain dedication.

from hypothesis import given, strategies as st


@given(seq=st.one_of(st.binary(), st.binary().map(bytearray), st.lists(st.integers())))
def test_idempotent_timsort(seq):
    result = timsort(seq=seq)
    repeat = timsort(seq=result)
    assert result == repeat, (result, repeat)

hypothesis.extra.ghostwriter.roundtrip(*funcs, except_=(), style='pytest', annotate=None)[source]¶

Write source code for a property-based test of funcs.

The resulting test checks that if you call the first function, pass the result to the second (and so on), the final result is equal to the first input argument.

This is a very powerful property to test, especially when the config options are varied along with the object to round-trip. For example, try ghostwriting a test for python:json.dumps() - would you have thought of all that?

hypothesis write --roundtrip json.dumps json.loads

hypothesis.extra.ghostwriter.equivalent( *funcs, allow_same_errors=False, except_=(), style='pytest', annotate=None, )[source]¶

Write source code for a property-based test of funcs.

The resulting test checks that calling each of the functions returns an equal value. This can be used as a classic ‘oracle’, such as testing a fast sorting algorithm against the python:sorted() builtin, or for differential testing where none of the compared functions are fully trusted but any difference indicates a bug (e.g. running a function on different numbers of threads, or simply multiple times).

The functions should have reasonably similar signatures, as only the common parameters will be passed the same arguments - any other parameters will be allowed to vary.

If allow_same_errors is True, then the test will pass if calling each of the functions returns an equal value, or if the first function raises an exception and each of the others raises an exception of the same type. This relaxed mode can be useful for code synthesis projects.

hypothesis.extra.ghostwriter.binary_operation( func, *, associative=True, commutative=True, identity=Ellipsis, distributes_over=None, except_=(), style='pytest', annotate=None, )[source]¶

Write property tests for the binary operation func.

While binary operations are not particularly common, they have such nice properties to test that it seems a shame not to demonstrate them with a ghostwriter. For an operator f, test that:

if associative, f(a, f(b, c)) == f(f(a, b), c)
if commutative, f(a, b) == f(b, a)
if identity is not None, f(a, identity) == a
if distributes_over is +, f(a, b) + f(a, c) == f(a, b+c)

For example:

ghostwriter.binary_operation(
    operator.mul,
    identity=1,
    distributes_over=operator.add,
    style="unittest",
)

hypothesis.extra.ghostwriter.ufunc(func, *, except_=(), style='pytest', annotate=None)[source]¶

Write a property-based test for the array ufunc func.

The resulting test checks that your ufunc or gufunc has the expected broadcasting and dtype casting behaviour. You will probably want to add extra assertions, but as with the other ghostwriters this gives you a great place to start.

hypothesis write numpy.matmul

A note for test-generation researchers¶

Ghostwritten tests are intended as a starting point for human authorship, to demonstrate best practice, help novices past blank-page paralysis, and save time for experts. They may be ready-to-run, or include placeholders and # TODO: comments to fill in strategies for unknown types. In either case, improving tests for their own code gives users a well-scoped and immediately rewarding context in which to explore property-based testing.

By contrast, most test-generation tools aim to produce ready-to-run test suites… and implicitly assume that the current behavior is the desired behavior. However, the code might contain bugs, and we want our tests to fail if it does! Worse, tools require that the code to be tested is finished and executable, making it impossible to generate tests as part of the development process.

Fraser 2013 found that evolving a high-coverage test suite (e.g. Randoop, EvoSuite, Pynguin) “leads to clear improvements in commonly applied quality metrics such as code coverage [but] no measurable improvement in the number of bugs actually found by developers” and that “generating a set of test cases, even high coverage test cases, does not necessarily improve our ability to test software”. Invariant detection (famously Daikon; in PBT see e.g. Alonso 2022, QuickSpec, Speculate) relies on code execution. Program slicing (e.g. FUDGE, FuzzGen, WINNIE) requires downstream consumers of the code to test.

Ghostwriter inspects the function name, argument names and types, and docstrings. It can be used on buggy or incomplete code, runs in a few seconds, and produces a single semantically-meaningful test per function or group of functions. Rather than detecting regressions, these tests check semantic properties such as encode/decode or save/load round-trips, for commutative, associative, and distributive operations, equivalence between methods, array shapes, and idempotence. Where no property is detected, we simply check for ‘no error on valid input’ and allow the user to supply their own invariants.

Evaluations such as the SBFT24 competition measure performance on a task which the Ghostwriter is not intended to perform. I’d love to see qualitative user studies, such as PBT in Practice for test generation, which could check whether the Ghostwriter is onto something or tilting at windmills. If you’re interested in similar questions, drop me an email!

Observability¶

Note

The Tyche VSCode extension provides an in-editor UI for observability results generated by Hypothesis. If you want to view observability results, rather than programmatically consume or display them, we recommend using Tyche.

Warning

This feature is experimental, and could have breaking changes or even be removed without notice. Try it out, let us know what you think, but don’t rely on it just yet!

Motivation¶

Understanding what your code is doing - for example, why your test failed - is often a frustrating exercise in adding some more instrumentation or logging (or print() calls) and running it again. The idea of observability is to let you answer questions you didn’t think of in advance. In slogan form,

Debugging should be a data analysis problem.

By default, Hypothesis only reports the minimal failing example… but sometimes you might want to know something about all the examples. Printing them to the terminal with verbose output might be nice, but isn’t always enough. This feature gives you an analysis-ready dataframe with useful columns and one row per test case, with columns from arguments to code coverage to pass/fail status.

This is deliberately a much lighter-weight and task-specific system than e.g. OpenTelemetry. It’s also less detailed than time-travel debuggers such as rr or pytrace, because there’s no good way to compare multiple traces from these tools and their Python support is relatively immature.

Configuration¶

If you set the HYPOTHESIS_EXPERIMENTAL_OBSERVABILITY environment variable, Hypothesis will log various observations to jsonlines files in the .hypothesis/observed/ directory. You can load and explore these with e.g. pd.read_json(".hypothesis/observed/*_testcases.jsonl", lines=True), or by using the sqlite-utils and datasette libraries:

sqlite-utils insert testcases.db testcases .hypothesis/observed/*_testcases.jsonl --nl --flatten
datasette serve testcases.db

If you are experiencing a significant slow-down, you can try setting HYPOTHESIS_EXPERIMENTAL_OBSERVABILITY_NOCOVER instead; this will disable coverage information collection. This should not be necessary on Python 3.12 or later.

Collecting more information¶

If you want to record more information about your test cases than the arguments and outcome - for example, was x a binary tree? what was the difference between the expected and the actual value? how many queries did it take to find a solution? - Hypothesis makes this easy.

event() accepts a string label, and optionally a string or int or float observation associated with it. All events are collected and summarized in Test statistics, as well as included on a per-test-case basis in our observations.

target() is a special case of numeric-valued events: as well as recording them in observations, Hypothesis will try to maximize the targeted value. Knowing that, you can use this to guide the search for failing inputs.

Data Format¶

We dump observations in json lines format, with each line describing either a test case or an information message. The tables below are derived from this machine-readable JSON schema, to provide both readable and verifiable specifications.

Note that we use python:json.dumps() and can therefore emit non-standard JSON which includes infinities and NaN. This is valid in JSON5, and supported by some JSON parsers including Gson in Java, JSON.parse() in Ruby, and of course in Python.

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.

If this causes problems, you can avoid loading the plugin with the -p no:hypothesispytest option.

Test statistics¶

Note

While test statistics are only available under pytest, you can use the observability interface to view similar information about your tests.

You can see a number of statistics about 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 tells you why Hypothesis stopped generating new examples. This is typically because we hit max_examples, but occasionally because we exhausted the search space or because shrinking was taking a very long time. This can be useful for understanding the behaviour of your tests.

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

hypothesis[cli]¶

Note

This feature requires the hypothesis[cli] extra, via pip install hypothesis[cli].

$ hypothesis --help
Usage: hypothesis [OPTIONS] COMMAND [ARGS]...

Options:
  --version   Show the version and exit.
  -h, --help  Show this message and exit.

Commands:
  codemod  `hypothesis codemod` refactors deprecated or inefficient code.
  fuzz     [hypofuzz] runs tests with an adaptive coverage-guided fuzzer.
  write    `hypothesis write` writes property-based tests for you!

This module requires the click package, and provides Hypothesis’ command-line interface, for e.g. ‘ghostwriting’ tests via the terminal. It’s also where HypoFuzz adds the hypothesis fuzz command (learn more about that here).

hypothesis[codemods]¶

Note

This feature requires the hypothesis[codemods] extra, via pip install hypothesis[codemods].

hypothesis[dpcontracts]¶

Note

This feature requires the hypothesis[dpcontracts] extra, via pip install hypothesis[dpcontracts].

Tip

For new projects, we recommend using either deal or icontract and icontract-hypothesis over dpcontracts. They’re generally more powerful tools for design-by-contract programming, and have substantially nicer Hypothesis integration too!