Read Latest Documentation - Browse GitHub Code Repository
hypothesis-auto is an extension for the Hypothesis project that enables fully automatic tests for type annotated functions.
Key Features:
- Type Annotation Powered: Utilize your function's existing type annotations to build dozens of test cases automatically.
- Low Barrier: Start utilizing property-based testing in the lowest barrier way possible. Just run
auto_test(FUNCTION)to run dozens of test. - pytest Compatible: Like Hypothesis itself, hypothesis-auto has built-in compatibility with the popular pytest testing framework. This means that you can turn your automatically generated tests into individual pytest test cases with one line.
- Scales Up: As you find your self needing to customize your auto_test cases, you can easily utilize all the features of Hypothesis, including custom strategies per a parameter.
To get started - install hypothesis-auto into your projects virtual environment:
pip3 install hypothesis-auto
OR
poetry add hypothesis-auto
OR
pipenv install hypothesis-auto
!!! warning
In old usage examples you will see _ prefixed parameters like _auto_verify=. This was done to avoid conflicting with existing function parameters.
Based on community feedback the project switched to _ suffixes, such as auto_verify_= to keep the likely hood of conflicting low while
avoiding the connotation of private parameters.
from hypothesis_auto import auto_test
def add(number_1: int, number_2: int = 1) -> int:
return number_1 + number_2
auto_test(add) # 50 property based scenarios are generated and ran against add
auto_test(add, auto_runs_=1_000) # Let's make that 1,000from hypothesis_auto import auto_test
def divide(number_1: int, number_2: int) -> int:
return number_1 / number_2
auto_test(divide)
-> 1012 raise the_error_hypothesis_found
1013
1014 for attrib in dir(test):
<ipython-input-2-65a3aa66e9f9> in divide(number_1, number_2)
1 def divide(number_1: int, number_2: int) -> int:
----> 2 return number_1 / number_2
3
0/0
ZeroDivisionError: division by zero
auto_test(divide, auto_allow_exceptions_=(ZeroDivisionError, ))from hypothesis_auto import Scenario, auto_test
def add(number_1: int, number_2: int = 1) -> int:
return number_1 + number_2
def my_custom_verifier(scenario: Scenario):
if scenario.kwargs["number_1"] > 0 and scenario.kwargs["number_2"] > 0:
assert scenario.result > scenario.kwargs["number_1"]
assert scenario.result > scenario.kwargs["number_2"]
elif scenario.kwargs["number_1"] < 0 and scenario.kwargs["number_2"] < 0:
assert scenario.result < scenario.kwargs["number_1"]
assert scenario.result < scenario.kwargs["number_2"]
else:
assert scenario.result >= min(scenario.kwargs.values())
assert scenario.result <= max(scenario.kwargs.values())
auto_test(add, auto_verify_=my_custom_verifier)Custom verification methods should take a single Scenario and raise an exception to signify errors.
For the full set of parameters, you can pass into auto_test see its API reference documentation.
from hypothesis_auto import auto_pytest_magic
def add(number_1: int, number_2: int = 1) -> int:
return number_1 + number_2
auto_pytest_magic(add)from hypothesis_auto import auto_pytest
def add(number_1: int, number_2: int = 1) -> int:
return number_1 + number_2
@auto_pytest()
def test_add(test_case, tmpdir):
tmpdir.mkdir().chdir()
test_case()from hypothesis_auto import Scenario, auto_pytest
def add(number_1: int, number_2: int = 1) -> int:
return number_1 + number_2
def my_custom_verifier(scenario: Scenario):
if scenario.kwargs["number_1"] > 0 and scenario.kwargs["number_2"] > 0:
assert scenario.result > scenario.kwargs["number_1"]
assert scenario.result > scenario.kwargs["number_2"]
elif scenario.kwargs["number_1"] < 0 and scenario.kwargs["number_2"] < 0:
assert scenario.result < scenario.kwargs["number_1"]
assert scenario.result < scenario.kwargs["number_2"]
else:
assert scenario.result >= min(scenario.kwargs.values())
assert scenario.result <= max(scenario.kwargs.values())
auto_pytest_magic(add, auto_verify_=my_custom_verifier)Custom verification methods should take a single Scenario and raise an exception to signify errors.
For the full reference of the pytest integration API see the API reference documentation.
I wanted a no/low resistance way to start incorporating property-based tests across my projects. Such a solution that also encouraged the use of type hints was a win/win for me.
I hope you too find hypothesis-auto useful!
~Timothy Crosley
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent