Quick start guide

This document should talk you through everything you need to get started with Hypothesis.

An example

Suppose we’ve written a run length encoding system and we want to test it out.

We have the following code which I took straight from the Rosetta Code wiki (OK, I removed some commented out code and fixed the formatting, but there are no functional modifications):

def encode(input_string):
    count = 1
    prev = ''
    lst = []
    for character in input_string:
        if character != prev:
            if prev:
                entry = (prev, count)
                lst.append(entry)
            count = 1
            prev = character
        else:
            count += 1
    else:
        entry = (character, count)
        lst.append(entry)
    return lst


def decode(lst):
    q = ''
    for character, count in lst:
        q += character * count
    return q

We want to write a test for this that will check some invariant of these functions.

The invariant one tends to try when you’ve got this sort of encoding / decoding is that if you encode something and then decode it then you get the same value back.

Lets see how you’d do that with Hypothesis:

from hypothesis import given
from hypothesis.strategies import text

@given(text())
def test_decode_inverts_encode(s):
    assert decode(encode(s)) == s

(For this example we’ll just let pytest discover and run the test. We’ll cover other ways you could have run it later).

The text function returns what Hypothesis calls a search strategy. An object with methods that describe how to generate and simplify certain kinds of values. The @given decorator then takes our test function and turns it into a parametrized one which, when called, will run the test function over a wide range of matching data from that strategy.

Anyway, this test immediately finds a bug in the code:

Falsifying example: test_decode_inverts_encode(s='')

UnboundLocalError: local variable 'character' referenced before assignment

Hypothesis correctly points out that this code is simply wrong if called on an empty string.

If we fix that by just adding the following code to the beginning of the function then Hypothesis tells us the code is correct (by doing nothing as you’d expect a passing test to).

if not input_string:
    return []

If we wanted to make sure this example was always checked we could add it in explicitly:

from hypothesis import given, example
from hypothesis.strategies import text

@given(text())
@example('')
def test_decode_inverts_encode(s):
    assert decode(encode(s)) == s

You don’t have to do this, but it can be useful both for clarity purposes and for reliably hitting hard to find examples. Also in local development Hypothesis will just remember and reuse the examples anyway, but there’s not currently a very good workflow for sharing those in your CI.

It’s also worth noting that both example and given support keyword arguments as well as positional. The following would have worked just as well:

@given(s=text())
@example(s='')
def test_decode_inverts_encode(s):
    assert decode(encode(s)) == s

Suppose we had a more interesting bug and forgot to reset the count each time. Say we missed a line in our encode method:

def encode(input_string):
  count = 1
  prev = ''
  lst = []
  for character in input_string:
      if character != prev:
          if prev:
              entry = (prev, count)
              lst.append(entry)
          # count = 1  # Missing reset operation
          prev = character
      else:
          count += 1
  else:
      entry = (character, count)
      lst.append(entry)
  return lst

Hypothesis quickly informs us of the following example:

Falsifying example: test_decode_inverts_encode(s='001')

Note that the example provided is really quite simple. Hypothesis doesn’t just find any counter-example to your tests, it knows how to simplify the examples it finds to produce small easy to understand ones. In this case, two identical values are enough to set the count to a number different from one, followed by another distinct value which should have reset the count but in this case didn’t.

The examples Hypothesis provides are valid Python code you can run. Any arguments that you explicitly provide when calling the function are not generated by Hypothesis, and if you explicitly provide all the arguments Hypothesis will just call the underlying function once rather than running it multiple times.

Installing

Hypothesis is available on pypi as “hypothesis”. You can install it with:

pip install hypothesis

If you want to install directly from the source code (e.g. because you want to make changes and install the changed version) you can do this with:

pip install -e .

You should probably run the tests first to make sure nothing is broken. You can do this with:

python setup.py test

Note that if they’re not already installed this will try to install the test dependencies.

You may wish to do all of this in a virtualenv. For example:

virtualenv venv
source venv/bin/activate
pip install hypothesis

Will create an isolated environment for you to try hypothesis out in without affecting your system installed packages.

Running tests

In our example above we just let pytest discover and run our tests, but we could also have run it explicitly ourselves:

if __name__ == '__main__':
    test_decode_inverts_encode()

We could also have done this as a unittest.TestCase:

import unittest

class TestEncoding(unittest.TestCase):
    @given(text())
    def test_decode_inverts_encode(self, s):
        self.assertEqual(decode(encode(s)), s)

if __name__ == '__main__':
    unittest.main()

A detail: This works because Hypothesis ignores any arguments it hasn’t been told to provide (positional arguments start from the right), so the self argument to the test is simply ignored and works as normal. This also means that Hypothesis will play nicely with other ways of parameterizing tests. e.g it works fine if you use pytest fixtures for some arguments and Hypothesis for others.

Writing tests

A test in Hypothesis consists of two parts: A function that looks like a normal test in your test framework of choice but with some additional arguments, and a @given decorator that specifies how to provide those arguments.

Here are some other examples of how you could use that:

from hypothesis import given
import hypothesis.strategies as st

@given(st.integers(), st.integers())
def test_ints_are_commutative(x, y):
    assert x + y == y + x

@given(x=st.integers(), y=st.integers())
def test_ints_cancel(x, y):
    assert (x + y) - y == x

@given(st.lists(st.integers()))
def test_reversing_twice_gives_same_list(xs):
    # This will generate lists of arbitrary length (usually between 0 and
    # 100 elements) whose elements are integers.
    ys = list(xs)
    ys.reverse()
    ys.reverse()
    assert xs == ys

@given(st.tuples(st.booleans(), st.text()))
def test_look_tuples_work_too(t):
    # A tuple is generated as the one you provided, with the corresponding
    # types in those positions.
    assert len(t) == 2
    assert isinstance(t[0], bool)
    assert isinstance(t[1], str)

Note that as we saw in the above example you can pass arguments to @given either as positional or as keywords.

Where to start

You should now know enough of the basics to write some tests for your code using Hypothesis. The best way to learn is by doing, so go have a try.

If you’re stuck for ideas for how to use this sort of test for your code, here are some good starting points:

  1. Try just calling functions with appropriate random data and see if they crash. You may be surprised how often this works. e.g. note that the first bug we found in the encoding example didn’t even get as far as our assertion: It crashed because it couldn’t handle the data we gave it, not because it did the wrong thing.
  2. Look for duplication in your tests. Are there any cases where you’re testing the same thing with multiple different examples? Can you generalise that to a single test using Hypothesis?
  3. This piece is designed for an F# implementation, but is still very good advice which you may find helps give you good ideas for using Hypothesis.

If you have any trouble getting started, don’t feel shy about asking for help.