2.2.9. Testing Python code with unittest, pytest, and Coverage

The goal of this tutorial to teach you how to effectively test and debug Python code.

Unit testing is a powerful methodology for testing and debugging code and ensuring that code works as intended. You can unit test your code by writing numerous tests of your code, each of which executes the code and assert that the expected result is produced.

To use unit testing effectively, it is best to begin by writing tests of individual pieces of your code, debugging each individual piece until they all work, and then proceeding to write larger tests of larger assemblies of code.

Collectively, your tests should cover all of the logic of your code. The following are the most popular metrics for evaluating the coverage of your tests. We recommend using statement coverage because this the simplest metric to evaluate and the most widely supported metric.

  • Statement/line coverage: This metric evaluates the fraction of the lines of your code which were evaluated during your tests and which specific lines were not evaluated by your tests. Statement coverage is the most popular coverage metric. Statement coverage can be assessed using the coverage and pytest-cov packages and the results can be analyzed using coverage, Coveralls, and Code Climate.

  • Branch coverage: This metric evalutes the fraction of the branches (if, elif, else, try, except, finally, for, while statements) of your code that were evaluated during your tests and which specific branches were not covered. Branch coverage can be a more reliable metric of the completeness of your coverage because it isn’t biased by large blocks of simple code. Branch coverage can be analyzed using the coverage package.

  • Decision/multiple condition converage: This metric evaluates the fraction of the conditions of your branches that were covered by your tests, and the specific conditions that were not covered. Decision coverage is a more thorough metric than branch coverage because it checks every condition of every branch. Decision coverage can be analyzed using the instrumental package.

  • Path coverage: This metric evaluates the fraction of paths of your code that were evaluated by your tests. This is a more thorough metric than decision coverage which is evaluates every combination of conditions. However, path coverage is not a practical metric due to the combinatorial number of paths in large codes. To our knowledge, there is no package for path coverage. Required packages

Execute the following commands to install the packages required for this tutorial:

apt-get install \
    python \
pip install \
    capturer \
    cement \
    coverage \
    numpy \
    pytest \
    pytest-cov \ File naming and organization

Our convention is to store tests within separate tests subdirectories within each repository. Any non-Python files which are needed for testing, can be organized in a fixtures subdirectory. The tests directory should also contain a requirements.txt file which lists all of the packages that are needed to run the tests.

Often it is helpful create one file for all of the tests for each source code file and to name this test_<source_filename>.py.

Taken together, your test code should be organized as follows:

    tests/                              # directory for all of the test files
        <source_modulename>             # parallel directory structure to source code
            test_<source_filename>.py   # parallel filenames to source code
        fixtures/                       # files needed by the tests
        requirements.txt                # list of packages needed to run the tests Writing tests

In the remainder of the tutorial, we will write tests for the code located in /path/to/this/repo/intro_to_wc_modeling/concepts_skills/software_engineering/unit_testing/ to run a simple stochastic simulation.

  1. Create a file for the tests, tests/concepts_skills/software_engineering/unit_testing/test_core.py

  2. Write a test file. For example:

    from intro_to_wc_modeling.concepts_skills.software_engineering.unit_testing import core
    import unittest
    class TestSimulation(unittest.TestCase):
        class NewClass():
        def setUpClass(cls):
            """ Code to execute before all of the tests. For example, this can be used to create temporary
        def tearDownClass(cls):
            """ Code to execute after all of the tests. For example, this can be used to clean up temporary
        def setUp(self):
            """ Code to execute before each test. """
        def tearDown(self):
            """ Code to execute after each test. """
        def test_run(self):
            # run code
            sim = core.Simulation()
            hist = sim.run(time_max=10)
            # check the result
            self.assertEqual(hist.times[0], 0.)

Each test method should begin within the prefix test_

unittest provides numerous assertions such as those below that can be used to verify that code produces the expected results. See the unittest documentation and the numpy.testing documentation for additional assertions.

  • unittest.TestCase.assertEqual

  • unittest.TestCase.assertNotEqual

  • unittest.TestCase.assertTrue

  • unittest.TestCase.assertFalse

  • unittest.TestCase.assertIsInstance

  • unittest.TestCase.assertGreater

  • unittest.TestCase.assertLess

  • unittest.TestCase.assertAlmostEqual

  • unittest.TestCase.assertRaises

  • numpy.testing.assert_array_equal

  • numpy.testing.assert_array_almost_equal

The setUp and tearDown methods can be used to organize the code that should be executed before and after each individual test. This is often useful for creating and removing temporary files. Similarly, the setUpClass and tearDownClass can be used to organize code that should be executed before and after the execution of all of the tests. This can be helpful to organizing computationally expensive operations that don’t need to be executed multiple times. Testing stochastic algorithms

Stochastic codes should be validated by testing the statistical distribution of their output. Typically this is done with the following process

  1. Run the code many times and keep a list of the outputs

  2. Run a statistical test of the distribution of the outputs. At a minimum test the average of the distribution is close to the expected value. If possible, also test the variance of the distribution and higher-order moments of the distribution. Testing standard output

The capturer package is helpful for collecting and testing stdout generated by code. This can be used to test standard output as shown in the example below:

import capturer

def test_stdout(self):
    with capturer.CaptureOutput() as captured:
        stdout = captured.get_text()
        self.assertEqual(stdout, expected) Testing cement command line programs

Cement command line programs can be tested as illustrated below:

from intro_to_wc_modeling import __main__ import capturer

def test(self):

# array of command line arguments, just as they would be supplied at the command line except # each should be an element of an array arv = [‘value’]

with __main__.App(argv=argv) as app:
with capturer.CaptureOutput() as captured:

app.run() self.assertEqual(captured.get_text(), expected_value)

See [tests/test_main.py](tests/test_main.py) for an annotated example. Testing for multiple version of Python

You should test your code on both major versions of Python. This can be done as follows:

python2 -m pytest tests
python3 -m pytest tests Running your tests

You can use pytest as follows to run all or a subset of your tests:

python -m pytest tests                                         # run all tests in a directory
python -m pytest tests/test_core.py                            # run all tests in a file
python -m pytest tests/test_core.py::TestSimulation            # run all tests in a class
python -m pytest tests/test_core.py::TestSimulation::test_run  # run a specific test
python -m pytest tests -k run                                  # run tests that contain `run` in their name
python -m pytest tests -s                                      # use the `-s` option to display the stdout generated by the tests Analyzing the coverage of your tests

Test coverage can be analyzed as follows:

python -m pytest –cov=intro_to_wc_modeling tests

This prints a summary of the coverage to the console and saves the details to .coverage.

The following can be used to generated a more detailed HTML coverage report. The report will be saved to htmlcov/:

coverage html

You can view the HTML report by opening file:///path/to/intro_to_wc_modeling/htmlcov/index.html in your browser. Green indicates lines that were executed by the tests. Red indicates lines that were not executed. Large amounts of red lines means that more tests are needed. Ideally, code would be tested to 100% coverage. Additional tutorials

There are numerous additional tutorials on unit testing Python