An Exploratory Evaluation of Code Smell Agglomerations

Abstract Context. Code smell is a symptom of decisions about the system design or code that may degrade its modularity. For example, they may indicate inheritance misuse, excessive coupling and size. When two or more code smells occur in the same snippet of code, they form a code smell agglomeration. Objective. Few studies evaluate how agglomerations may impact code modularity. In this work, we evaluate which aspects of modularity are being hindered by agglomerations. This way, we can support practitioners in improving their code, by refactoring the code involved with code smell agglomeration that was found as harmful to the system modularity. Method. We analyze agglomerations composed of four types of code smells: Large Class, Long Method, Feature Envy, and Refused Bequest. We then conduct a comparison study between 20 systems mined from the Qualita Corpus dataset with 10 systems mined from GitHub. In total, we analyzed 1,789 agglomerations in 30 software projects, from both repositories: Qualita Corpus and GitHub. We rely on frequent itemset mining and non-parametric hypothesis testing for our analysis. Results. Agglomerations formed by two or more Feature Envy smells have a significant frequency in the source code for both repositories. Agglomerations formed by different smell types impact the modularity more than classes with only one smell type and classes without smells. For some metrics, when Large Class appears alone, it has a significant and large impact when compared to classes that have two or more method-level smells of the same type. Conclusion. We have identified which agglomerations are more frequent in the source code, and how they may impact the code modularity. Consequently, we provide supporting evidence of which agglomerations developers should refactor to improve the code modularity.