GFI-Bot: Automated Good First Issue Recommendation on GitHub

Published: November 14, 2022

Authors: Hao He, Haonan Su, Wenxin Xiao, Runzhi He, and Minghui Zhou
Venue: The 2022 ACM 30th Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Links: [DOI] [BibTeX] [PDF] [Code]

Cite As

@inproceedings{DBLP:conf/sigsoft/HeSXHZ22,
  author       = {Hao He and
                  Haonan Su and
                  Wenxin Xiao and
                  Runzhi He and
                  Minghui Zhou},
  editor       = {Abhik Roychoudhury and
                  Cristian Cadar and
                  Miryung Kim},
  title        = {GFI-bot: automated good first issue recommendation on GitHub},
  booktitle    = {Proceedings of the 30th {ACM} Joint European Software Engineering
                  Conference and Symposium on the Foundations of Software Engineering,
                  {ESEC/FSE} 2022, Singapore, Singapore, November 14-18, 2022},
  pages        = {1751--1755},
  publisher    = {{ACM}},
  year         = {2022},
  url          = {https://doi.org/10.1145/3540250.3558922},
  doi          = {10.1145/3540250.3558922},
  timestamp    = {Sun, 19 Jan 2025 13:13:21 +0100},
  biburl       = {https://dblp.org/rec/conf/sigsoft/HeSXHZ22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Abstract

To facilitate newcomer onboarding, GitHub recommends the use of "good first issue" (GFI) labels to signal issues suitable for newcomers to resolve. However, previous research shows that manually labeled GFIs are scarce and inappropriate, calling the need for automated recommendations. In this paper, we present GFI-Bot (accessible at https://gfibot.io), a proof-of-concept machine learning powered bot for automated GFI recommendation in practice. Project maintainers can configure GFI-Bot to discover and label possible GFIs so that newcomers can easily locate issues for making their first contributions. GFI-Bot also provides a high-quality, up-to-date dataset for advancing GFI recommendation research.