OpenSourceLens: Explainable, Newcomer-Friendly Issue Recommendation and Contribution Success Prediction in Open-Source Projects

OpenSourceLens, an explainable, newcomer-friendly issue recommendation and contribution success prediction system for open-source projects.

OpenSourceLens, an explainable, newcomer-friendly issue recommendation and contribution success prediction system for open-source projects. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to practise engineering discipline: requirements, design, implementation, automated testing and measuring software quality.

The main parts are explanations of each result (feature importance or visual evidence), personalised recommendations ranked by relevance, a clear requirements and design document, the working implementation, and automated tests with quality metrics. The reference build uses SHAP, Python, Git, Docker, and pytest, with a clear project structure, setup instructions and sample data so that it can be run and extended.