代码审查中的重基被认为是有害的:一项大规模的实证调查

M. Paixão, P. Maia
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引用次数: 10

摘要

在开放源代码和工业软件开发中,代码审查被广泛认为是一个关键的质量保证过程。由于代码审查过程的异步性,当审查源代码更改时,系统的代码库倾向于合并外部提交,这导致需要重基操作。外部提交有可能修改当前正在审查的文件,这会导致开发人员重新工作,并使审查者感到疲劳。由于在代码审查期间观察到的源代码更改可能是由于外部提交,重基操作可能对使用代码审查数据的经验研究构成严重威胁。然而,据我们所知,在现实世界的软件系统中,没有对重基进行表征和调查的实证研究。因此,本文报告了一项实证调查,旨在了解重基操作发生的频率及其在审查过程中的副作用。为此,我们对11个软件系统的代码审查数据、28,808次代码审查和99,121次修订进行了深入的大规模实证调查。我们的观察表明开发人员需要在平均75.35%的代码审查中执行重基操作。此外,我们的数据表明,平均34.21%的改基操作倾向于篡改审查过程。最后,我们提出了一种在使用代码审查数据的实证研究中处理重基的方法。我们展示了一项不考虑改基操作的实证研究如何报告扭曲、有偏见和不准确的观察结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rebasing in Code Review Considered Harmful: A Large-Scale Empirical Investigation
Code review has been widely acknowledged as a key quality assurance process in both open-source and industrial software development. Due to the asynchronicity of the code review process, the system's codebase tends to incorporate external commits while a source code change is reviewed, which cause the need for rebasing operations. External commits have the potential to modify files currently under review, which causes re-work for developers and fatigue for reviewers. Since source code changes observed during code review may be due to external commits, rebasing operations may pose a severe threat to empirical studies that employ code review data. Yet, to the best of our knowledge, there is no empirical study that characterises and investigates rebasing in real-world software systems. Hence, this paper reports an empirical investigation aimed at understanding the frequency in which rebasing operations occur and their side-effects in the reviewing process. To achieve so, we perform an in-depth large-scale empirical investigation of the code review data of 11 software systems, 28,808 code reviews and 99,121 revisions. Our observations indicate that developers need to perform rebasing operations in an average of 75.35% of code reviews. In addition, our data suggests that an average of 34.21% of rebasing operations tend to tamper with the reviewing process. Finally, we propose a methodology to handle rebasing in empirical studies that employ code review data. We show how an empirical study that does not account for rebasing operations may report skewed, biased and inaccurate observations.
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