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Why Security Defects Go Unnoticed during Code Reviews? A Case-Control Study of the Chromium OS Project

机译:为什么安全缺陷在代码审核期间不受注意到? 铬OS项目的病例对照研究

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Peer code review has been found to be effective in identifying security vulnerabilities. However, despite practicing mandatory code reviews, many Open Source Software (OSS) projects still encounter a large number of post-release security vulnerabilities, as some security defects escape those. Therefore, a project manager may wonder if there was any weakness or inconsistency during a code review that missed a security vulnerability. Answers to this question may help a manager pinpointing areas of concern and taking measures to improve the effectiveness of his/her project’s code reviews in identifying security defects. Therefore, this study aims to identify the factors that differentiate code reviews that successfully identified security defects from those that missed such defects.With this goal, we conduct a case-control study of Chromium OS project. Using multi-stage semi-automated approaches, we build a dataset of 516 code reviews that successfully identified security defects and 374 code reviews where security defects escaped. The results of our empirical study suggest that the are significant differences between the categories of security defects that are identified and that are missed during code reviews. A logistic regression model fitted on our dataset achieved an AUC score of 0.91 and has identified nine code review attributes that influence identifications of security defects. While time to complete a review, the number of mutual reviews between two developers, and if the review is for a bug fix have positive impacts on vulnerability identification, opposite effects are observed from the number of directories under review, the number of total reviews by a developer, and the total number of prior commits for the file under review.
机译:已发现对等代码审查有效地识别安全漏洞。但是,尽管练习强制性代码审查,许多开源软件(OSS)项目仍然遇到大量发布后的安全漏洞,因为某些安全缺陷逃脱了那些。因此,项目经理可能想知道在遗漏安全漏洞的代码审查中是否存在任何弱点或不一致。这个问题的答案可以帮助经理确定关注和采取措施,以提高他/她的项目规范审查的效果识别安全缺陷。因此,本研究旨在确定区分规范审查的因素,以便从这些目标中成功地确定了那些错过了这种缺陷的安全缺陷。在此目标中,我们进行了对铬OS项目的案例控制研究。使用多级半自动化方法,我们构建一个516个代码审查的数据集,成功确定了安全缺陷和374个代码审查,其中安全缺陷逃逸。我们的实证研究结果表明,在审查期间错过的安全缺陷类别之间存在显着差异。在我们的数据集上拟合的逻辑回归模型实现了0.91的AUC分数,并确定了影响安全缺陷标识的9个代码审查属性。虽然时间来完成审查,但两个开发人员之间的相互审查数量,以及审查是针对BUG修复对漏洞识别产生积极影响,从审查的目录数量方面观察到相反的影响,开发人员,以及正在审查的文件的先前提交总数。

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