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genty

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Genty, pronounced "gen-tee", stands for "generate tests". It promotes generative testing, where a single test can execute over a variety of input. Genty makes this a breeze.

For example, consider a file sample.py containing both the code under test and the tests:

from box.test.genty import genty, genty_repeat, genty_dataset
from unittest import TestCase

# Here's the class under test
class MyClass(object):
    def add_one(self, x): 
        return x + 1

# Here's the test code
@genty
class MyClassTests(TestCase):
    @genty_dataset(
        (0, 1),
        (100000, 100001),
    )
    def test_add_one(self, value, expected_result):
        actual_result = MyClass().add_one(value)
        self.assertEqual(expected_result, actual_result)

Running the MyClassTests using the default unittest runner

$ python -m unittest -v sample
test_add_one(0, 1) (sample.MyClassTests) ... ok
test_add_one(100000, 100001) (sample.MyClassTests) ... ok

----------------------------------------------------------------------
Ran 2 tests in 0.000s

OK

Instead of having to write multiple independent tests for various test cases, code can be refactored and parametrized using genty!

It produces readable tests. It produces maintainable tests. It produces expressive tests.

Another option is running the same test multiple times. This is useful in stress tests or when excerising code looking for race conditions. This particular test

@genty_repeat(3)
def test_adding_one_to_zero(self):
    self.assertEqual(1, MyClass().add_one(0))

would be run 3 times, producing output like

$ python -m unittest -v sample
test_adding_one() iteration_1 (sample.MyClassTests) ... ok
test_adding_one() iteration_2 (sample.MyClassTests) ... ok
test_adding_one() iteration_3 (sample.MyClassTests) ... ok

----------------------------------------------------------------------
Ran 3 tests in 0.001s

OK

The 2 techniques can be combined:

@genty_repeat(2)
@genty_dataset(
    (0, 1),
    (100000, 100001),
)
def test_add_one(self, value, expected_result):
    actual_result = MyClass().add_one(value)
    self.assertEqual(expected_result, actual_result)

There are more options to explore including naming your datasets and genty_args.

@genty_dataset(
    default_case=(0, 1),
    limit_case=(999, 1000),
    error_case=genty_args(-1, -1, is_something=False),
)
def test_complex(self, value1, value2, optional_value=None, is_something=True):
    ...

would run 3 tests, producing output like

$ python -m unittest -v sample
test_complex(default_case) (sample.MyClassTests) ... ok
test_complex(limit_case) (sample.MyClassTests) ... ok
test_complex(error_case) (sample.MyClassTests) ... ok

----------------------------------------------------------------------
Ran 3 tests in 0.003s

OK

genty_args allow you to define the params to the test method as if it were being called directly. Thus for complex tests with lots of parameters, one can take advantage of default values and named parameters.

Enjoy!

Installation

To install, simply:

pip install genty

Contributing

See CONTRIBUTING.

Setup

Create a virtual environment and install packages -

mkvirtualenv genty
pip install -r requirements-dev.txt

Testing

Run all tests using -

tox

The tox tests include code style checks via pep8 and pylint.

Copyright and License

Copyright 2014 Box, Inc. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.