Feature requests are a key communication mechanism on GitHub, enabling users and developers to collaboratively shape the direction of open-source projects. Feature requests are prevalent and important, but have been underexplored in existing studies. There is limited understanding of how they are labeled, how they evolve, and how they are resolved. A deeper understanding of feature requests is critical, not only for improving issue triage and project management but also for fostering more effective collaboration within open-source communities. In this work, we present the first systematic and large-scale empirical study of feature requests. Drawing on 1.4 million issues from 825 GitHub repositories, we examine how feature requests are labeled, how their submission and backlog patterns change over a project’s lifecycle, how they differ from other types of issues in terms of resolution and engagement, and what factors contribute to their successful handling. Our findings reveal that labeling practices are often inconsistent across projects, that feature requests follow distinct temporal trends, and that those which are lengthy and contain large code snippets tend to be more difficult to resolve. By contrast, concise and clearly defined requests, particularly those submitted by experienced contributors and accompanied by active discussions, are more likely to be addressed. This study underscores the challenges of managing feature requests at scale and provides practical insights for maintainers, contributors, and researchers. To support future work in this area, we publicly release our dataset and analysis results.

@inproceedings{paperbot9535,
  title = {Understanding Feature Request Practice on GitHub via a Large-Scale Empirical Study},
  author = {Jiajun Li and Wenhua Yang and Minxue Pan and Yu Zhou},
  booktitle = {ASE},
  year = {2025},
  doi = {10.1109/ase63991.2025.00228}
}