Code context-based reviewer recommendation  

作　　者：Dawei YUAN Xiao PENG Zijie CHEN Tao ZHANG Ruijia LEI 

机构地区：[1]School of Computer Science and Engineering,Macao University of Science and Technology,Macao 999078,China [2]Faculty of Science,University of Amsterdam,Amsterdam WB 1018,Netherlands

出　　处：《Frontiers of Computer Science》2025年第1期97-108,共12页计算机科学前沿(英文版)

基　　金：supported in part by the Science and Technology Development Fund(FDCT),Macao SAR,China(Nos.0047/2020/A1 and 0014/2022/A).

摘　　要：Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.

关 键 词：code reviewer recommendation code context pull request 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]