一句话：将队列研究引入软件仓库分析，检验代码坏味道对类变更与故障倾向的因果效应。
论文关注代码坏味道是否真的导致类更易变更或更易出错。以往仓库挖掘研究多报告相关性，难以推断因果。作者将流行病学中的队列研究设计实例化到软件仓库研究，复用 Khomh 等人的数据，考察 11 类代码坏味道对类变更倾向和故障倾向的因果影响。结果表明，总体而言代码坏味道会显著增加类的变更倾向；但对故障倾向，除少数例外，未发现显著影响。该工作部分证实、部分挑战了既有结论，强调相关不等于因果，并为在其他软件属性和关系上采用队列研究设计奠定基础。
Code smells are suboptimal design choices in source code that are conjectured to hinder software maintainability. Previous research has studied the impact of code smells on class change- and fault-proneness, yet, as in most mining studies, it has been limited to highlighting correlations rather than detecting causal relationships.This paper instantiates the cohort study design to software repository studies and presents a cohort study aimed at assessing whether code smells cause an increase or decrease in class change- and fault-proneness.By reusing data from the work of Khomh et al. [41] within a cohort study design, we investigate the causal effect of eleven types of code smells on class change- and fault-proneness.Our results show that, overall, the presence of code smells appears to be causing a significant increase in change-proneness, while, with few exceptions, fault-proneness appears unaffected. This work reinforces the principle that correlation does not imply causation: we partially confirmed some previous results on the effects of code smells, while challenging others. It also lays the groundwork for applying cohort study designs to other software properties and relationships previously studied using observational data.
@inproceedings{paperbot11891,
title = {Causal or Correlational? A Cohort Study on the Effects of Code Smells on Class Change- and Fault-Proneness},
author = {Sabato Nocera and Sira Vegas and Giuseppe Scanniello and Massimiliano Di Penta and Natalia Juristo},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3787786}
}