Towards Improving Code Review Effectiveness Through Task Automation
Towards Improving Code Review Effectiveness Through Task Automation
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DOI：
10.1145/3551349.3559565
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Asif Kamal Turzo
中科院分区：
文献类型：
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作者：
Asif Kamal Turzo
现代代码评审（MCR）是当今软件行业广泛采用的软件质量保证实践。由于软件开发人员在MCR活动上花费了大量的时间，即使MCR有效性的微小改进也会带来显著的节省。由于大多数MCR活动严重依赖手工工作，因此有很大的机会通过工具支持来提高效率。为了应对这些挑战，我提出的论文的主要目标是提高现代代码审查的有效性与审查员选择和错误识别的自动化。为此，我提出三项研究。第一项研究的目的是调查有用的MCR的概念和影响MCR有用性的因素。第二项研究的目的是开发一个审稿人推荐系统，利用审稿人以前的历史，在类似的情况下提供有用的反馈。最后，第三项研究旨在通过利用在以前的审查中发现的错误来提高静态分析工具的有效性。
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.