Beyond Stars and Commits: A Fair and Manipulation-Resistant Framework for Measuring Developer Contributions

Evaluating software contributions remains a challenge, especially with the rise of AI-assisted development. Existing methods often conflate contributions with recognition, making assessments susceptible to bias and manipulation. This paper proposes a structured framework that distinguishes contributions from recognition, integrates predictive modeling with reinforcement learning, and incorporates anomaly detection to ensure fairness and robustness. By dynamically refining contribution scores over time, the framework adapts to delayed recognition signals while mitigating artificial inflation. Future work will focus on empirical validation, resistance to manipulation, and improving assessment transparency for both open-source and enterprise software development.