Revisiting the Impact of Anti-patterns on Fault-Proneness: A Differentiated Replication

Aurel Ikama, Vincent Du, Philippe Belias, B. Muse, Foutse Khomh, Mohammad Hamdaqa
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引用次数: 1

Abstract

Anti-patterns manifesting on software code through code smells have been investigated in terms of their prevalence, detection, refactoring, and impact on software quality attributes. In particular, leveraging heuristics to identify fault-fixing commits, Khomh et al. have found that anti-patterns and code smells have an impact on the fault-proneness of a software system. Similarly, Saboury et al. found a relationship between anti-pattern occurrences and fault-proneness, using heuristic to identify fault-fixing commits and fault-inducing changes. However, recent studies question the accuracy of heuristics, and thus the validity of empirical studies that leverage it. Hence, in this work, we would like to investigate to what extent the results of empirical studies using heuristics to identify bug fix commits are affected by the limitations of the heuristics based approach using manually validated bug fix commits as a ground truth. In particular, we conduct a differentiated replication of the work by Khomh et al. We particularly focused on the impact of anti-patterns on fault-proneness as it is the only dependent variable that may be affected by noise in the collected faults data. In our differentiated replication study, (1) we expanded the number of subject systems from 5 to 38, (2) utilized a manually validated dataset of bug-fixing commits from the work of Herbold et al., and (3) answered research questions from Khomh et al., that are related to the relationship between anti-pattern occurrences and fault-proneness. (4) We added an additional research question to investigate if combining results from several heuristic-based approaches could help reduce the impact of noise. Our findings show that the impact of the noise generated by the automatic algorithm heuristic based is negligible for the studied subject systems; meaning that the reported relation observed on noisy data still holds on the clean data. However, we also observed that combining results from several heuristic based approaches do not reduce this noise, quite the contrary.
重新审视反模式对错误倾向的影响:差异化复制
通过代码气味在软件代码上表现的反模式已经在它们的流行、检测、重构和对软件质量属性的影响方面进行了研究。特别是,利用启发式方法来识别错误修复提交,Khomh等人发现反模式和代码气味对软件系统的错误倾向有影响。类似地,Saboury等人发现了反模式发生和错误倾向之间的关系,他们使用启发式方法来识别修复错误的提交和导致错误的更改。然而,最近的研究质疑启发式的准确性,因此利用它的实证研究的有效性。因此,在这项工作中,我们想要调查使用启发式方法来识别错误修复提交的实证研究结果在多大程度上受到基于启发式方法的局限性的影响,该方法使用手动验证的错误修复提交作为基本真理。特别是,我们对Khomh等人的工作进行了差异化的复制。我们特别关注反模式对故障倾向的影响,因为它是唯一可能受到收集到的故障数据中的噪声影响的因变量。在我们的差异化复制研究中,(1)我们将受试者系统的数量从5个扩展到38个,(2)利用了Herbold等人的工作中手动验证的bug修复提交数据集,(3)回答了Khomh等人的研究问题,这些问题与反模式发生和错误倾向之间的关系有关。(4)我们增加了一个额外的研究问题,以调查结合几种基于启发式方法的结果是否有助于减少噪声的影响。研究结果表明,基于启发式的自动算法产生的噪声对研究对象系统的影响可以忽略不计;这意味着在噪声数据上观察到的报告关系仍然适用于干净数据。然而,我们也观察到,结合几种基于启发式方法的结果并不能减少这种噪音,恰恰相反。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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