Improving Issue-PR Link Prediction via Knowledge-Aware Heterogeneous Graph Learning

Shuotong Bai

doi:10.1109/TSE.2024.3408448

2024-07-01

PRE

Shuotong Bai

刘华虓 (Huaxiao Liu) *

Lei Liu

DOI：10.1109/TSE.2024.3408448

原文链接

原文求助

摘要

En 中文

Links between issues and pull requests (PRs) assist GitHub developers in tackling technical challenges, gaining development inspiration, and improving repository maintenance. In realistic repositories, these links are still insufficiently established. Aiming at this situation, existing works focus on issues and PRs themselves and employ text similarity with additional information like issue size to predict issue-PR links, yet their effectiveness is unsatisfactory. The limitation is that issues and PRs are not isolated on GitHub. Rather, they are related to multiple GitHub sources, including repositories and submitters, which, through their diverse relationships, can supply potential and crucial knowledge about technical domains, developmental insights, and cross-repository technical details. To this end, we propose Auto IP Linker (AIPL), which introduces the heterogeneous graph to model multiple GitHub sources with their relationships. Further, it leverages the metapath-based technique to reveal and incorporate the potential information for a more comprehensive understanding of issues and PRs. Firstly, we identify 4 types of GitHub sources related to issues and PRs (repositories, users, issues, PRs) as well as their relationships, and model them into task-specific heterogeneous graphs. Next, we analyze information transmitted among issues or PRs to reveal which knowledge is crucial for them. Based on our analysis, we formulate a series of metapaths and employ the metapath-based technique to incorporate various information for learning the knowledge-aware embedding of issues and PRs. Finally, we can infer whether an issue and a PR can be linked based on their embedding. We evaluate the performance of AIPL on real-world data sets collected from GitHub. The results show that, compared to the baselines, AIPL can achieve average improvements of 15.94%, 15.19%, 20.52%, and 18.50% in terms of Accuracy, Precision, Recall, and F1-score.

Keyword:

Software development management

Task analysis

Open source software

Firewire

Codes

Maintenance

Information processing

GitHub

issue-PR links

issue systems

pull request