The Influence of Code Smells in Efferent Neighbors on Class Stability

TL;DR

This paper investigates how code smells in classes that a given class depends on influence its stability, especially considering code smell interrelation and interaction, using empirical data from GitHub projects.

Contribution

It introduces an empirical analysis of the impact of efferent neighbor code smells, interrelation, and interaction on class stability, expanding understanding beyond the class itself.

Findings

Code smells in efferent neighbors affect class stability.

Code smell interrelation and interaction exacerbate stability issues.

Empirical evidence from GitHub projects supports these effects.

Abstract

Understanding what drives code instability is essential for effective software maintenance, as unstable classes require larger or more frequent edits and increase the risk of unintended side effects. Although code smells are widely believed to harm maintainability, most prior stability studies examine only the smells within the class being modified. In practice, however, classes can change because their efferent neighbors (i.e., the classes they depend on) are modified due to ripple effects that propagate along static dependencies, even if the class itself is clean. Such ripple effects may be more severe when the efferent neighbor exhibits code smells. In addition, code smells rarely occur alone. They often appear together within a class or across classes connected by static dependencies, a phenomenon known as code smell interrelation. Such interrelation can lead to code smell interaction, where smells are directly connected through static dependencies and may further compound maintainability issues. However, the effect of code smell interrelation and interaction on code quality remains largely underexplored. Therefore, this study investigates whether the presence of code smells in a class's efferent neighbors affects its stability, considering the factor of code smell interrelation and interaction. To achieve this, we mine one year of commit history from 100 top-starred GitHub projects, detect code smells and static dependencies, determine code smell interrelation and interaction, and model these factors as predictors of class stability.