Setting Up CI and Writing Tests for Another Project

This week, I continued improving the testing setup for my Repository-Context-Packager tool and worked on adding tests to my partner’s project. This was good practice in managing project complexity using automated testing and Continuous Integration.

To automate test execution, I added a GitHub Actions CI workflow. The workflow runs on every push and on every pull request. It uses an Ubuntu runner, installs a C++ compiler, builds the project and its tests, and executes all Catch2 test cases. If any test fails, the workflow stops and reports the failure in the pull request. Setting this up helped me understand how automated test pipelines work and how CI ensures the main branch never breaks.

For the collaboration part of the lab, I contributed a test to my partner's Python project. Their repository was structured differently, and there wasn’t a single obvious entry point for testing, so I had to explore the package and understand how the modules were organized before writing a test. My contribution was a dynamic import test tests/test_imports.py that walks through every module under the analyzer package and confirms that each one imports correctly. The test automatically discovers all submodules using pkgutil.walk_packages, tries importing them with importlib.import_module(), and reports any failures. This ensures the package structure stays consistent and that no module contains syntax errors or missing dependencies.

Working on someone else’s codebase required slowing down and reading the design closely, not just looking at individual lines. It felt closer to debugging or performing a code review, because I had to decide what the expected behavior of their modules should be before creating the test. Overall, this week strengthened my understanding of automated testing, CI workflows, and how to approach testing in unfamiliar projects.