Enhancing the modular structure of existing systems has attracted substantial research interest, primarily through (1) software modularization and (2) identifying design issues (e.g., smells) as refactoring opportunities; however, both approaches often prove impractical to guide effective improvement. Inspired by both aforementioned approaches, our previous study introduced a novel and practical architectural smell -- called Pairwise Modular Smell (or PairSmell) -- for identifying flawed architectural decisions that necessitate further examination. The objective of this study is to explain PairSmell from the perspective of pair characteristics. To this end, we first conduct a rapid review to collect and synthesize 19 pair characteristics that have been used in the literature to represent relationships between two entities. The collected characteristics are then used to train machine learning models for predicting two forms of PairSmell -- inapt separated pairs InSep and inapt collocated pairs InCol, based on a curated dataset of over 6,135,000 pairs of entities derived from 11 open-source Java projects. The trained models achieve up to a 58.6% improvement in ROC-AUC over the baselines. The interpretation of the models reveals that the most influential features for InSep are out-going dependencies, terms shared with others, and declared fields; while those for InCol include semantic similarity based on tf-idf, terms shared between the pair, terms shared with others, and in-going dependencies. We complement the work with a series of practical examples to illustrate how the influential pair characteristics impact the occurrence of PairSmell.

翻译：暂无翻译