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Allows you to run a test with multiple data sets

Project description

Upcoming Breaking Change!

When Genty was released through version 0.2.0, it was released under the namespace box.test. In version 0.3.0, importing genty became easier:

from genty import genty, genty_dataset, genty_args


from box.test.genty import genty, genty_dataset, genty_args
from box.test.genty.genty_args import genty_args

In version 1.0.0, however, you will no longer be able to import genty from box.test.


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 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
class MyClassTests(TestCase):
        (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


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

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


The 2 techniques can be combined:

    (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.

    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


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.



To install, simply:

pip install genty




Create a virtual environment and install packages -

mkvirtualenv genty
pip install -r requirements-dev.txt


Run all tests using -


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

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Filename, size & hash SHA256 hash help File type Python version Upload date
genty-0.3.1.tar.gz (16.0 kB) Copy SHA256 hash SHA256 Source None Aug 29, 2014

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