genty

About

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 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 exercising 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

The @genty_datasets can be chained together. This is useful, for example, if there are semantically different datasets so keeping them separate would help expressiveness.

@genty_dataset(10, 100)
@genty_dataset('first', 'second')
def test_composing(self, parameter_value):
    ...

would run 4 tests, producing output like

$ python -m unittest -v sample
test_composing(10) (sample.MyClassTests) ... ok
test_composing(100) (sample.MyClassTests) ... ok
test_composing(u'first') (sample.MyClassTests) ... ok
test_composing(u'second') (sample.MyClassTests) ... ok

----------------------------------------------------------------------
Ran 4 tests in 0.000s

OK

Sometimes the parameters to a test can\'t be determined at module load time. For example, some test might be based on results from some http request. And first the test needs to authenticate, etc. This is supported using the @genty_dataprovider decorator like so:

def setUp(self):
    super(MyClassTests, self).setUp()

    # http authentication happens
    # And imagine that _some_function is actually some http request
    self._some_function = lambda x, y: ((x + y), (x - y), (x * y))

@genty_dataset((1000, 100), (100, 1))
def calculate(self, x_val, y_val):
    # when this is called... we've been authenticated
    return self._some_function(x_val, y_val)

@genty_dataprovider(calculate)
def test_heavy(self, data1, data2, data3):
    ...

would run 4 tests, producing output like

$ python -m unittest -v sample
test_heavy_calculate(100, 1) (sample.MyClassTests) ... ok
test_heavy_calculate(1000, 100) (sample.MyClassTests) ... ok

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

OK

Notice here how the name of the helper (calculate) is added to the names of the 2 executed test cases.

Like @genty_dataset, @genty_dataprovider can be chained together.

Enjoy!

Deferred Parameterization

Parametrized tests where the final parameters are not determined until test execution time. It looks like so:

@genty_dataset((1000, 100), (100, 1))
def calculate(self, x_val, y_val):
    # when this is called... we've been authenticated, perhaps in
    # some Test.setUp() method.

    # Let's imagine that _some_function requires that authentication.
    # And it returns a 3-tuple, matching that signature of
    # of the test(s) decorated with this 'calculate' method.
    return self._some_function(x_val, y_val)

@genty_dataprovider(calculate)
def test_heavy(self, data1, data2, data3):
    ...

The calculate() method is called 2 times based on the @genty_dataset decorator, and each of it\'s return values define the final parameters that will be given to the method test_heavy(...).

Installation

To install, simply:

pip install genty

Contributing

See CONTRIBUTING.rst.

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.

The tox tests are configured to run on Python 2.6, 2.7, 3.3, 3.4, 3.5, and PyPy 2.6.

Copyright and License

Copyright 2015 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.

Contributing

All contributions are welcome to this project.

Contributor License Agreement

Before a contribution can be merged into this project, please fill out the Contributor License Agreement (CLA) located at:

http://box.github.io/cla

To learn more about CLAs and why they are important to open source projects, please see the Wikipedia entry.

How to contribute

• File an issue - if you found a bug, want to request an enhancement, or want to implement something (bug fix or feature).
• Send a pull request - if you want to contribute code. Please be sure to file an issue first.

Pull request best practices

We want to accept your pull requests. Please follow these steps:

Step 1: File an issue

Before writing any code, please file an issue stating the problem you want to solve or the feature you want to implement. This allows us to give you feedback before you spend any time writing code. There may be a known limitation that can\'t be addressed, or a bug that has already been fixed in a different way. The issue allows us to communicate and figure out if it\'s worth your time to write a bunch of code for the project.

Step 2: Fork this repository in GitHub

This will create your own copy of our repository.

Step 3: Add the upstream source

The upstream source is the project under the Box organization on GitHub. To add an upstream source for this project, type:

git remote add upstream git@github.com:box/genty.git

This will come in useful later.

Step 4: Create a feature branch

Create a branch with a descriptive name, such as add-search.

Step 5: Push your feature branch to your fork

As you develop code, continue to push code to your remote feature branch. Please make sure to include the issue number you\'re addressing in your commit message, such as:

git commit -am "Adding search (fixes #123)"

This helps us out by allowing us to track which issue your commit relates to.

Keep a separate feature branch for each issue you want to address.

Step 6: Rebase

Before sending a pull request, rebase against upstream, such as:

git fetch upstream
git rebase upstream/master

This will add your changes on top of what\'s already in upstream, minimizing merge issues.

Step 7: Run the tests

Make sure that all tests are passing before submitting a pull request.

Step 8: Send the pull request

Send the pull request from your feature branch to us. Be sure to include a description that lets us know what work you did.

Keep in mind that we like to see one issue addressed per pull request, as this helps keep our git history clean and we can more easily track down issues.