DRCI: A Developer Recommendation Method for Issues Using Comprehensive Information

With the rapid development of Open Source Software (OSS), open-source platforms represented by GitHub have become the core hub for global developer collaboration and innovation. However, the explosive growth of open-source projects, especially the surge in the number of issues, has posed challenges for developers. On one hand, developers face the problem of information overload when selecting issues to make contributions; on the other hand, maintainers are confronted with the difficulty of finding efficient and rational issue assignment methods to achieve effective issue resolution. To address these challenges, researchers have proposed various methods for recommending developers for issues. However, existing studies have the following limitations: (1) Single information utilization; (2) Insufficient dynamic modeling; (3) Lack of the impact of developer inactivity periods. To tackle these problems, this paper proposes a developer recommendation method DRCI that integrates multidimensional information. By modeling developers' preferences for issue attributes over time and leveraging KGCN for data information mining, DRCI significantly enhances the recommendation performance, providing a more efficient solution for issue management in open-source communities.