Two recent studies explicitly recommend labeling defective classes in releases using the affected versions (AV) available in issue trackers. The aim our study is threefold: 1) to measure the proportion of defects for which the realistic method is usable, 2) to propose a method for retrieving the AVs of a defect, thus making the realistic approach usable when AVs are unavailable, 3) to compare the accuracy of the proposed method versus three SZZ implementations. The assumption of our proposed method is that defects have a stable life cycle in terms of the proportion of the number of versions affected by the defects before discovering and fixing these defects. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, reveal that the realistic method cannot be used in the majority (51%) of defects. Therefore, it is important to develop automated methods to retrieve AVs. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes, affected by 60,000 defects, and spread over 4,000 versions and 760,000 commits, reveal that the proportion of the number of versions between defect discovery and fix is pretty stable (STDV < 2) across the defects of the same project. Moreover, the proposed method resulted significantly more accurate than all three SZZ implementations in (i) retrieving AVs, (ii) labeling classes as defective, and (iii) in developing defects repositories to perform feature selection. Thus, when the realistic method is unusable, the proposed method is a valid automated alternative to SZZ for retrieving the origin of a defect. Finally, given the low accuracy of SZZ, researchers should consider re-executing the studies that have used SZZ as an oracle and, in general, should prefer selecting projects with a high proportion of available and consistent AVs.

中文翻译：

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利用缺陷生命周期来标记受影响的版本和有缺陷的类

最近的两项研究明确建议使用问题跟踪器中可用的受影响版本 (AV) 标记版本中的缺陷类。我们的研究有三个目的：1) 测量可使用现实方法的缺陷比例，2) 提出一种检索缺陷 AV 的方法，从而使现实方法在 AV 不可用时可用，3)比较所提出的方法与三个 SZZ 实现的准确性。我们提出的方法的假设是，在发现和修复这些缺陷之前，就受缺陷影响的版本数量的比例而言，缺陷具有稳定的生命周期。与来自 Apache 生态系统的 212 个开源项目相关的结果，共有约 125,000 个缺陷，表明现实方法不能用于大多数 (51%) 缺陷。因此，开发自动化方法来检索 AV 非常重要。来自 Apache 生态系统的 76 个开源项目的相关结果，共约 6,250,000 个类，受 60,000 个缺陷影响，分布超过 4,000 个版本和 760,000 个提交，显示缺陷发现和修复之间的版本数比例为在同一项目的缺陷中相当稳定（STDV < 2）。此外，所提出的方法在 (i) 检索 AV、(ii) 将类标记为有缺陷和 (iii) 开发缺陷存储库以执行特征选择方面比所有三种 SZZ 实现都更准确。因此，当现实方法无法使用时，所提出的方法是 SZZ 检索缺陷来源的有效自动化替代方法。最后，鉴于 SZZ 的准确性较低，研究人员应考虑重新执行使用 SZZ 作为预言机的研究，并且一般而言，应选择具有高比例可用且一致的 AV 的项目。