Developing a Comprehensive Unit Test Framework for PorQua

[Konikz]

For GSoC'25 I would love to be mentored by and contribute to GeomScale, as a small contribution I focused on improving the reliability and maintainability of PorQua, by developing a structured and automated unit testing framework.

Key Contributions

1. Testing Infrastructure Setup

   • Implemented a structured pytest framework for automated testing.
   • Integrated GitHub Actions for continuous integration (CI), ensuring tests run automatically on every commit.

2. Expanded Test Coverage

   • Developed unit tests for core modules, including optimization algorithms, data handling, and backtesting/modeling functions.
   • Used mocking and fixtures to isolate dependencies and ensure stable test execution.

3. Performance & Edge Case Testing

   • Designed tests for large datasets to assess scalability and efficiency.
   • Ensured numerical stability and validated edge cases to prevent errors in extreme conditions.

4. Code Quality & Documentation

   • Enforced code coverage thresholds using pytest-cov to maintain high-quality standards.
   • Created comprehensive documentation for running tests and contributing to the framework.

Impact & Outcomes

• Established a reliable, automated testing ecosystem for PorQua, improving code stability.
• Increased test coverage across critical functionalities, reducing regression risks.
• CI/CD integration ensures continuous validation of new code contributions.
• Strengthened PorQua’s maintainability, facilitating future enhancements and contributions.