Tests make development easier for both veteran project contributors and newcomers alike. Most projects use the unittest framework for tests so you should familiarize yourself with this framework.

Writing tests can be a great way to get involved with a project. It’s an opportunity to get familiar with the codebase and the code submission and review process. Check the project’s code coverage and write a test for a piece of code missing coverage!

Patches should be accompanied by one or more tests to demonstrate the feature or bugfix works. This makes the review process much easier since it allows the reviewer to run your code with very little effort, and it lets developers know when they break your code.

Test Organization

Having a standard test layout makes it easy to find tests. When adding new tests, follow the following guidelines:

  1. Each module in the application should have a corresponding test module. These modules should be organized in the test package to mirror the package they test. That is, if the package contains the <package>/server/push.py module, the test module should be in a module called <test_root>/server/test_push.py.

  2. Within each test module, follow the unittest code organization guidelines.

  3. Include documentation blocks for each test case that explain the goal of the test.

  4. Avoid using mock unless absolutely necessary. It’s easy to write tests using mock that only assert that mock works as expected. When testing code that makes HTTP requests, consider using vcrpy.

You may find projects that do not follow this test layout. In those cases, consider re-organizing the tests to follow the layout described here and follow the established conventions for that project until that happens.

Test Runners

Projects should include a way to run the tests with ease locally and the steps to run the tests should be documented. This should be the same way the continuous integration (Jenkins, Zuul CI, etc.) tool runs the tests.

There are many test runners available that can discover unittest based tests. These include:

Projects should choose whichever runner best suits them.

You may find projects using the nose test runner. nose is in maintenance mode and, according to their documentation, will likely cease without a new maintainer. They recommend using unittest, pytest, or nose2.


Tox is an easy way to run your project’s tests (using a Python test runner) using multiple Python interpreters. It also allows you to define arbitrary test environments, so it’s an excellent place to run the code style tests and to ensure the project’s documentation builds without errors or warnings.

Here’s an example tox.ini file that runs a project’s unit tests in Python 2.7, Python 3.4, Python 3.5, and Python 3.6. It also runs flake8 on the entire codebase and builds the documentation with the warnings treated as errors Sphinx flag enabled. Finally, it enforces 100% coverage on lines edited by new patches using diff-cover:

envlist = py27,py34,py35,py36,lint,diff-cover,docs
# If the user is missing an interpreter, don't fail
skip_missing_interpreters = True

deps =
# Substitute your test runner of choice
commands =
# When running in OpenShift you don't have a username, so expanduser
# won't work. If you are running your tests in CentOS CI, this line is
# important so the tests can pass there, otherwise tox will fail to find
# a home directory when looking for configuration files.
passenv = HOME

deps =
commands =
    diff-cover coverage.xml --compare-branch=origin/master --fail-under=100

changedir = docs
deps =
whitelist_externals =
    mkdir -p _static
    sphinx-build -W -b html -d {envtmpdir}/doctrees .  _build/html

deps =
    flake8 > 3.0
commands =
    python -m flake8 {posargs}

show-source = True
max-line-length = 100
exclude = .git,.tox,dist,*egg


coverage is a good way to collect test coverage statistics. pytest has a pytest-cov plugin that integrates with coverage and nose-cov provides integration for the nose test runner. diff-cover can be used to ensure that all lines edited in a patch have coverage.

It’s possible (and recommended) to have the test suite fail if the coverage percentage goes down. This example .coveragerc:

# Track what conditional branches are covered.
branch = True
include =

# Fail if the coverage is not 100%
fail_under = 100
# Display results with up 1/100th of a percent accuracy.
precision = 2
exclude_lines =
    pragma: no cover

    # Don't complain if tests don't hit defensive assertion code
    raise AssertionError
    raise NotImplementedError

    if __name__ == .__main__.:
omit =

To configure pytest to collect coverage data on your project, edit setup.cfg and add this block, substituting yourpackage with the name of the Python package you are measuring coverage on:

addopts = --cov-config .coveragerc --cov=yourpackage --cov-report term --cov-report xml --cov-report html

This causes coverage (and any test running plugins using coverage) to fail if the coverage level is not 100%. New projects should enforce 100% test coverage. Existing projects should ensure test coverage does not drop to accept a pull request and should increase the minimum test coverage until it is 100%.

coverage has great exclusion support, so you can exclude individual lines, conditional branches, functions, classes, and whole source files from your coverage report. If you have code that doesn’t make sense to have tests for, you can exclude it from your coverage report. Remember to leave a comment explaining why it’s excluded!


The liccheck checker can verify that every dependency in your project has an acceptable license. The dependencies are checked recursively.

The licenses are validated against a set of acceptable licenses that you define in a file called .license_strategy.ini in your project directory. Here is an example of such a file, that would accept Free licenses:

        new bsd
        simplified bsd
        apache 2.0
        apache software
        gnu lgpl
        gpl v2
        gpl v3
        lgpl with exceptions or zpl
        isc license (iscl)
        python software foundation
        zpl 2.1

The verification is case-insensitive, and is done on both the license and the classifiers metadata fields. See liccheck's documentation for more details.

You can automate the license check with the following snippet in your tox.ini file:

deps =
commands =
    liccheck -s .license_strategy.ini

Remember to add licenses to your Tox envlist.


The bandit checker is designed to find common security issues in Python code.

You can add it to the tests run by Tox by adding the following snippet to your tox.ini file:

deps = bandit
commands =
    bandit -r your_project/ -x your_project/tests/ -ll

Remember to add bandit to your Tox envlist.