开源社区拉取请求与问题的链接建立方法

In the GitHub open-source community, developers contribute code to projects by submitting pull requests (PRs). Some PRs are linked to issues reported by users, indicating an intent to address those issues. Maintaining these linking relationships is essential for enhancing project traceability and accountability. However, the current manual approach to established links is not only time-consuming and labor-intensive but also susceptible to omissions owing to the high volume of PRs and issues. To address these challenges, this study introduces LinkFinder, a method designed to establish links between PRs and issues efficiently. LinkFinder mitigates the impact of highly homogenized content in templates through filtering, extracts semantic and statistical features from PRs and issues, and utilizes a deep neural network to calculate the matching degree between PRs and issues; this process generates a recommended list of linked issues for each PR. We conducted experiments using 25411 links from five open-source projects. The results demonstrate that LinkFinder achieves a mean average precision (MAP) ranging from 0.434 to 0.774, a mean reciprocal rank (MRR) ranging from 0.436 to 0.774, a Top-1 Precision ranging from 0.344 to 0.702, a Top-1 Recall ranging from 0.333 to 0.698, and a Top-1 F1 score ranging from 0.338 to 0.700. Compared to the baseline method, T-BERT, LinkFinder improves MAP by 9.01% to 186.63%, MRR by 8.86% to 183.50%, Top-1 Precision by 20.21% to 388.54%, Top-1 Recall by 20.34% to 386.52%, and Top-1 F1 score by 20.27% to 389.13%. To evaluate the practical value of established links, this study further proposes a reviewer recommendation method based on linked issue participation. Experimental results show that incorporating reviewers' participation in linked issues improves MAP by 3.11% to 41.20% and MRR by 2.45% to 49.26% across four projects compared to the baseline method, RevFinder.