一句话：提出混合评审人推荐方法 CORMS，融合路径、项目/子项目、作者相似性与变更主题预测，显著提升 RevFinder 的推荐准确率。
现代代码评审中，评审人选择随团队规模增长而愈发困难，错误推荐会增加时间与成本。本文扩展 RevFinder 基线，针对新建文件、已离职评审人、外部效度和准确率问题，提出混合方法 CORMS。CORMS 通过相似性分析计算文件路径、项目/子项目、作者信息等相似度，并结合基于变更主题的预测模型推荐评审人。作者在 Gerrit 和 GitHub 的 20 个常用项目上与 RevFinder 对比，CORMS 平均 top-1、top-3、top-5、top-10 准确率和 MRR 分别为 45.1%、67.5%、74.6%、79.9% 和 0.58，相对 RevFinder 分别提升 44.9%、34.4%、20.8%、12.3% 和 18.4%。
Modern Code review (MCR) techniques are widely adopted in both open-source software platforms and organizations to ensure the quality of their software products. However, the selection of reviewers for code review is cumbersome with the increasing size of development teams. The recommendation of inappropriate reviewers for code review can take more time and effort to complete the task effectively. In this paper, we extended the baseline of reviewers' recommendation framework - RevFinder - to handle issues with newly created files, retired reviewers, the external validity of results, and the accuracies of the state-of-the-art RevFinder. Our proposed hybrid approach, CORMS, works on similarity analysis to compute similarities among file paths, projects/sub-projects, author information, and prediction models to recommend reviewers based on the subject of the change. We conducted a detailed analysis on the widely used 20 projects of both Gerrit and GitHub to compare our results with RevFinder. Our results reveal that on average, CORMS, can achieve top-1, top-3, top-5, and top-10 accuracies, and Mean Reciprocal Rank (MRR) of 45.1%, 67.5%, 74.6%, 79.9% and 0.58 for the 20 projects, consequently improves the RevFinder approach by 44.9%, 34.4%, 20.8%, 12.3% and 18.4%, respectively.
DOI 原文 ·
@inproceedings{paperbot7006,
title = {CORMS: a GitHub and Gerrit based hybrid code reviewer recommendation approach for modern code review},
author = {Prahar Pandya and Saurabh Tiwari},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549115}
}