一句话：提出基于检索的 CommentFinder，自动推荐代码审查评论，准确率更高且速度提升 49 倍。
代码审查是有效的质量保证实践，但开发者需手动审查并撰写反馈，耗时费力。现有基于深度学习的评论推荐方法效果虽好，却依赖高昂计算资源和时间，难以实用。本文提出 CommentFinder：一种基于检索的方法，根据变更方法自动推荐代码审查评论。作者在 151,019 个变更方法上与最先进方法进行实证评估。结果表明，推荐最佳-1评论候选时，CommentFinder 比先前工作准确率提高 32%，且速度提升 49 倍。这说明它能在减少人工审查负担的同时显著降低计算时间。
Code review is an effective quality assurance practice, but can be labor-intensive since developers have to manually review the code and provide written feedback. Recently, a Deep Learning (DL)-based approach was introduced to automatically recommend code review comments based on changed methods. While the approach showed promising results, it requires expensive computational resource and time which limits its use in practice. To address this limitation, we propose CommentFinder – a retrieval-based approach to recommend code review comments. Through an empirical evaluation of 151,019 changed methods, we evaluate the effectiveness and efficiency of CommentFinder against the state-of-the-art approach. We find that when recommending the best-1 review comment candidate, our CommentFinder is 32% better than prior work in recommending the correct code review comment. In addition, CommentFinder is 49 times faster than the prior work. These findings highlight that our CommentFinder could help reviewers to reduce the manual efforts by recommending code review comments, while requiring less computational time.
DOI 原文 ·
@inproceedings{paperbot6973,
title = {CommentFinder: a simpler, faster, more accurate code review comments recommendation},
author = {Yang Hong and Chakkrit Tantithamthavorn and Patanamon Thongtanunam and Aldeida Aleti},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549119}
}