Quickstart¶
This is a lightning introduction to the most important features of Hypothesis; enough to get you started writing tests. The tutorial introduces these features (and more) in greater detail.
Install Hypothesis¶
pip install hypothesis
Write your first test¶
Create a new file called example.py, containing a simple test:
# contents of example.py
from hypothesis import given, strategies as st
@given(st.integers())
def test_integers(n):
print(f"called with {n}")
assert isinstance(n, int)
test_integers()
@given is the standard entrypoint to Hypothesis. It takes a strategy, which describes the type of inputs you want the decorated function to accept. When we call test_integers, Hypothesis will generate random integers (because we used the integers() strategy) and pass them as n. Let’s see that in action now by running python example.py:
called with 0
called with -18588
called with -672780074
called with 32616
...
We can just call test_integers(), without passing a value for n, because Hypothesis takes care of generating values of n for us.
Note
By default, Hypothesis generates 100 random examples. You can control this with the max_examples setting.
Running in a test suite¶
A Hypothesis test is still a regular python function, which means pytest or unittest will pick it up and run it in all the normal ways.
# contents of example.py
from hypothesis import given, strategies as st
@given(st.integers(0, 200))
def test_integers(n):
assert n < 50
This test will clearly fail, which can be confirmed by running pytest example.py:
$ pytest example.py
...
@given(st.integers())
def test_integers(n):
> assert n < 50
E assert 50 < 50
E Falsifying example: test_integers(
E n=50,
E )
Arguments to @given¶
You can pass multiple arguments to @given:
@given(st.integers(), st.text())
def test_integers(n, s, b1, b2):
assert isinstance(n, int)
assert isinstance(s, str)
Or use keyword arguments:
@given(n=st.integers(), s=st.text())
def test_integers(n, s):
assert isinstance(n, int)
assert isinstance(s, str)
Filtering inside a test¶
Sometimes, you need to remove invalid cases from your test. The best way to do this is with .filter():
@given(st.integers().filter(lambda n: n % 2 == 0))
def test_integers(n):
assert n % 2 == 0
For more complicated conditions, you can use assume(), which tells Hypothesis to discard any test case with a false-y argument:
@given(st.integers(), st.integers())
def test_integers(n1, n2):
assume(n1 != n2)
# n1 and n2 are guaranteed to be different here
Note
You can learn more about .filter() and assume() in the Adapting strategies tutorial page.
Dependent generation¶
You may want an input to depend on the value of another input. For instance, you might want to generate two integers n1 and n2 where n1 <= n2.
You can do this using the @composite strategy. @composite lets you define a new strategy which is itself built by drawing values from other strategies, using the automatically-passed draw function.
@st.composite
def ordered_pairs(draw):
n1 = draw(st.integers())
n2 = draw(st.integers(min_value=n1))
return (n1, n2)
@given(ordered_pairs())
def test_pairs_are_ordered(pair):
n1, n2 = pair
assert n1 <= n2
In more complex cases, you might need to interleave generation and test code. In this case, use data().
@given(st.data(), st.text(min_size=1))
def test_string_characters_are_substrings(data, string):
assert isinstance(string, str)
index = data.draw(st.integers(0, len(string) - 1))
assert string[index] in string
Combining Hypothesis with pytest¶
Hypothesis works with pytest features, like pytest.mark.parametrize:
Hypothesis also works with pytest fixtures:
import pytest
@pytest.fixture(scope="session")
def shared_mapping():
return {n: 0 for n in range(101)}
@given(st.integers(0, 100))
def test_shared_mapping_keys(shared_mapping, n):
assert n in shared_mapping