Software Clustering Using Dynamic Analysis and Static Dependencies

Abstract

Decomposing a software system into smaller, more manageable clusters is a common approach to support the comprehension of large systems. In recent years, researchers have focused on clustering techniques to perform such architectural decomposition, with the most predominant clustering techniques relying on the static analysis of source code. We argue that these static structural relationships are not sufficient for software clustering due to the increased complexity and behavioral aspects found in software systems. In this paper, we present a novel software clustering approach that combines dynamic and static analysis to identify component clusters. We introduce a two-phase clustering technique that combines software features to build a core skeleton decomposition with structural information to further refine these clusters. A case study is presented to evaluate the applicability and effectiveness of our approach.