5.4.1 Writing Unit Tests for the test package

Python PEP

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5.4.1 Writing Unit Tests for the `test` package

It is preferred that tests for the test package use the unittest module and follow a few guidelines. One is to have the name of all the test methods start with "test_" as well as the module's name. This is needed so that the methods are recognized by the test driver as test methods. Also, no documentation string for the method should be included. A comment (such as "# Tests function returns only True or False") should be used to provide documentation for test methods. This is done because documentation strings get printed out if they exist and thus what test is being run is not stated.

A basic boilerplate is often used:

import unittest
from test import test_support

class MyTestCase1(unittest.TestCase):

    # Only use setUp() and tearDown() if necessary

    def setUp(self):
        ... code to execute in preparation for tests ...

    def tearDown(self):
        ... code to execute to clean up after tests ...

    def test_feature_one(self):
        # Test feature one.
        ... testing code ...

    def test_feature_two(self):
        # Test feature two.
        ... testing code ...

    ... more test methods ...

class MyTestCase2(unittest.TestCase):
    ... same structure as MyTestCase1 ...

... more test classes ...

def test_main():
    test_support.run_unittest(MyTestCase1,
                              MyTestCase2,
                              ... list other tests ...
                             )

if __name__ == '__main__':
    test_main()

This boilerplate code allows the testing suite to be run by test.regrtest as well as on its own as a script.

The goal for regression testing is to try to break code. This leads to a few guidelines to be followed:

The testing suite should exercise all classes, functions, and constants. This includes not just the external API that is to be presented to the outside world but also "private" code.
Whitebox testing (examining the code being tested when the tests are being written) is preferred. Blackbox testing (testing only the published user interface) is not complete enough to make sure all boundary and edge cases are tested.
Make sure all possible values are tested including invalid ones. This makes sure that not only all valid values are acceptable but also that improper values are handled correctly.
Exhaust as many code paths as possible. Test where branching occurs and thus tailor input to make sure as many different paths through the code are taken.
Add an explicit test for any bugs discovered for the tested code. This will make sure that the error does not crop up again if the code is changed in the future.
Make sure to clean up after your tests (such as close and remove all temporary files).
Import as few modules as possible and do it as soon as possible. This minimizes external dependencies of tests and also minimizes possible anomalous behavior from side-effects of importing a module.

Try to maximize code reuse. On occasion, tests will vary by something as small as what type of input is used. Minimize code duplication by subclassing a basic test class with a class that specifies the input:

class TestFuncAcceptsSequences(unittest.TestCase):

    func = mySuperWhammyFunction

    def test_func(self):
        self.func(self.arg)

class AcceptLists(TestFuncAcceptsSequences):
    arg = [1,2,3]

class AcceptStrings(TestFuncAcceptsSequences):
    arg = 'abc'

class AcceptTuples(TestFuncAcceptsSequences):
    arg = (1,2,3)