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.

``` python

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