How to Collect Code Coverage in Python from External Libraries

Discover how to measure `code coverage` in Python, including external libraries, using the Coverage module effectively.
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Measuring Code Coverage in Python from External Libraries

When it comes to automation testing in Python, ensuring that your tests provide sufficient code coverage is crucial. Code coverage helps you understand how much of your code is exercised by your tests, and ideally, you want to cover both your project and any external libraries used during testing. However, many developers wonder: Is it possible to collect code coverage from external libraries using the Coverage module?

In this post, we will explore how to implement this functionality and cover all aspects of measuring code coverage for external libraries.

The Challenge

The main issue arises when using external libraries in your Python project. By default, the Coverage module only reports on code coverage within your own project's code, which can lead to a gap in your test coverage assessments.

Example Scenario

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In this case, you're testing a method from the built-in random library. However, you might wonder how to collect code coverage information for the random module itself, as it's an external library.

Solution: Using the Coverage Module

To enable code coverage reporting for external libraries, you'll need to adjust how you run the Coverage module. Here's how to do it step-by-step:

Step 1: Install the Coverage Module

Make sure you have the Coverage module installed in your environment. If it’s not installed, you can add it via pip:

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Step 2: Run Coverage with the --pylib Option

To measure coverage inside external Python libraries, you can use the --pylib option available in the Coverage command line. Here’s how you can run it:

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Explanation of the Command

coverage run: This is the command that starts recording coverage metrics.

--pylib: This option tells the Coverage module to include coverage data for libraries installed in your Python environment.

Step 3: Generate the Coverage Report

After running the tests, you can generate a report to see your coverage results. Simply run the following command:

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Or, for a more detailed HTML report, use:

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This will create a htmlcov directory containing an HTML report that you can view in your browser.

Conclusion

Measuring code coverage from external libraries in Python is not only possible but also straightforward. By using the --pylib option with the Coverage module, you can ensure you have a comprehensive view of your code's coverage, including all dependencies.

Now, you'll have more confidence in the robustness of your automation testing tool by ensuring that it measures not just your code, but also how it interacts with external libraries. Happy testing!