Testing Python code using Unittests

Why Test Your Code?

This video explains the benefits of using Python testing when programming. This is intended for makers who are coding software for physical computing, hobby software programmers and those looking to learn about software programming through school, college, university or adults looking to increase their skills. Without any automated testing bugs can be harder to find and fix.

While formal testing is often associated with critical systems like nuclear power stations or aviation, it is equally valuable for personal projects. Implementing tests can make the development process more enjoyable by reducing the time spent on frustrating manual debugging. Key benefits include:

Targeted Debugging: Splitting code into modules and testing them separately allows you to trust your data sources and pinpoint exactly where a problem lies.

Safe Iteration: Running a suite of tests every time you change your code ensures that new updates haven't broken existing functionality.

Goal Setting: Testing provides a sense of satisfaction similar to gaming—achieving a "pass" on a test provides a clear target and a rewarding feeling of progress.

Practical Implementation with unittest

The Python unittest library is a powerful built-in tool for creating simple, automated tests.

Basic Test Structure

To test a class or module, you create a new test file that imports the unittest library and the code you wish to verify.

Class Inheritance: Your test class must extend unittest.TestCase to be recognized by the framework.

Assertions: Use methods like assertEqual to check if values match, or assertTrue/assertFalse to verify logic outcomes.

Automated Execution: By calling unittest.main(), the script will automatically find and run every test method defined in the class.

Managing Test Files

As projects grow, it is best practice to keep tests in a separate directory. A simple "runner" script in your main directory can be used to import and execute all tests across different modules simultaneously.

Advanced Testing Concepts

For more comprehensive testing, consider the following strategies:

Test-Driven Development (TDD): A structured approach where you write tests before writing the actual code to define your objectives clearly.

Handling Edge Cases: Focus tests on extreme values or unusual scenarios, such as date calculations that span across midnight or month boundaries.

Code Coverage: Evaluate how much of your code is actually being exercised by your tests to ensure no logic paths are left unverified.

Testing Hooks: You can add optional arguments to your functions to "override" variables like the current time, allowing you to test scenarios that would otherwise be difficult to simulate.