一句话：提出三项研究，通过自动化审阅者选择与缺陷识别来提升现代代码审查的有效性。
现代代码审查（MCR）是软件质量保障的重要实践，但开发者投入大量时间且流程高度依赖人工，因此即使小幅提升有效性也能带来显著收益。该博士研究计划旨在通过任务自动化提升 MCR 有效性，重点解决审阅者选择与缺陷识别两个环节。作者提出三项研究：首先探究什么是有用的 MCR 及其影响因素；其次开发审阅者推荐系统，利用审阅者在相似上下文中提供有用反馈的历史记录进行推荐；最后利用先前审查中发现的缺陷来增强静态分析工具的有效性。摘要未给出具体实验结果，预期贡献是工具支持与实证理解。
Modern code review (MCR) is a widely adopted software quality assurance practice in the contemporary software industry. As software developers spend significant amounts of time on MCR activities, even a small improvement in MCR effectiveness will incur significant savings. As most of the MCR activities are heavily dependent on manual work, there are significant opportunities to improve effectiveness through tool support. To address the challenges, the primary objective of my proposed dissertation is to improve the effectiveness of modern code reviews with the automation of reviewer selection and bug identification. On this goal, I propose three studies. The first study aims to investigate the notion of useful MCRs and factors influencing MCR usefulness. The second study aims to develop a reviewer recommendation system that leverages a reviewer’s prior history of providing useful feedback under similar contexts. Finally, the third study aims to improve the effectiveness of static analysis tools by leveraging bugs identified during prior reviews.
DOI 原文 ·
@inproceedings{paperbot7336,
title = {Towards Improving Code Review Effectiveness Through Task Automation},
author = {Asif Kamal Turzo},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559565}
}