Search-Based Approaches for Software Module Clustering Based on Multiple Relationship Factors

Abstract

Software remodularization seeks to cluster software modules with high cohesion and low coupling: such a structure can help the comprehension and maintenance of complex systems. The modularization quality is usually captured using either structural, semantic, or history-based factors. All existing techniques apply a single factor to the entire system, which raises the following issues. First, a single factor may fail to capture the quality across the entire project: some modules may form semantic bondings, while others may form more structural ones. Second, the user of the technique has to choose a factor without knowing which one would perform the best. To resolve these issues, we propose a multi-factor module clustering, in which module clusters can be formed based on different factors. Our technique not only allows module clusters of different natures, but also relieve users from having to select a single factor. The paper introduces two different search-based formulations of multi-factor remodularization, and compares these against single-factor remodularization using four heterogeneous factors and six open source projects. The evaluation results show that the multi-factor remodularization can produce solutions that are 10.69% closer to the actual modularization adopted by the developers as compared with those produced by single-factor remodularization on average.