How to Improve Code Quality in Python Projects with Unit Test Coverage

Ensuring code quality is a crucial aspect of software development, and one effective way to achieve it is by adding unit test coverage to your Python projects. Code coverage helps you identify areas in your codebase that are untested, reducing the likelihood of bugs and improving overall software reliability. In this guide, we’ll explore how to add unit test coverage to your Python projects using the pytest-cov library.

Libraries

When it comes to collecting code coverage in Python, you have a couple of options. The raw library for collecting code coverage is coverage.py. However, if you’re already using pytest, a popular testing framework for Python, you can leverage the pytest-cov plugin, which integrates seamlessly with pytest and simplifies the process.

Installation of pytest-cov

Before we dive into collecting code coverage, make sure you have pytest installed. You can install pytest-cov and pytest using pip as follows:

Install with pip

pip install pytest-cov

Install with requirements.txt If you prefer to manage your project’s dependencies with a requirements file, add the following dev requirement:

pytest-cov == 4.0.0

Run unit tests

Now that you have pytest-cov installed, running your unit tests with code coverage is a breeze. Use the following command to a generate code coverage report:

python3 -m pytest -v --cov=my_code_under_test --cov-report html

And will produce stdout summary:

-------------------- coverage: ... ---------------------
Name                 Stmts   Miss  Cover
----------------------------------------
my_code_under_test/__init__          2      0   100%
my_code_under_test/myproj          257     13    94%
my_code_under_test/feature4286      94      7    92%
----------------------------------------
TOTAL                  353     20    94%

This summary provides an overview of code coverage for different parts of your project, helping you identify areas that may need more testing.

Ignore test files

In some cases, your test code may show up in the coverage report, which is not what you want. To exclude test files from the coverage report, follow these steps:

1. Create a file named .coveragerc in the root of your project directory.
2. In the .coveragerc file, add the following statement to omit your test code:

   [run]
   omit = 
    src/test/
    src/perf_test/    
   

By specifying the paths to omit, you ensure that only your production code is considered for coverage analysis.

Conclusion

In conclusion, adding unit test coverage to your Python projects using pytest-cov is a valuable practice to enhance code quality and identify untested code. By following the steps outlined in this guide, you can systematically improve your project’s reliability and maintainability.