A Two-Stage Approach for GitHub Issue Links Identification and Classification

Effective issue management is critical for the success of open-source projects on GitHub. However, the platform currently lacks the capability to identify implicit links between issues, complicating the management process. In this study, we propose a machine learning-based approach to identify and classify these links. Preliminary experimental results demonstrate the effectiveness of our approach in identifying issue links, indicating its potential to enhance issue tracking in large-scale GitHub projects.