Feature-Gathering Dependency-Based Software Clustering Using Dedication and Modularity

TL;DR

This paper introduces SArF, a novel dependency-based software clustering algorithm that automatically groups features without human intervention, improving over existing methods and nearly reaching the theoretical limits of clustering accuracy.

Contribution

SArF eliminates the need for human interaction in software clustering by using Dedication scores and Modularity Maximization, advancing automated feature-based clustering techniques.

Findings

SArF successfully decomposes systems aligning with feature-based decompositions.

SArF outperforms existing dependency-based clustering methods.

Clustering quality approaches theoretical limits.

Abstract

Software clustering is one of the important techniques to comprehend software systems. However, presented techniques to date require human interactions to refine clustering results. In this paper, we proposed a novel dependency-based software clustering algorithm, SArF. SArF has two characteristics. First, SArF eliminates the need of the omnipresent-module-removing step which requires human interactions. Second, the objective of SArF is to gather relevant software features or functionalities into a cluster. To achieve them, we defined the Dedication score to infer the importance of dependencies and utilized Modularity Maximization to cluster weighted directed graphs. Two case studies and extensive comparative evaluations using open source and industrial systems show that SArF could successfully decompose the systems fitting to the authoritative decompositions from a feature viewpoint without any tailored setups and that SArF was superior to existing dependency-based software clustering studies. Besides, the case studies show that there exist measurable authoritativeness limits and that SArF nearly reached the limits.