SOFTWARE ARCHITECTURE RECOVERY THROUGH SIMILARITY-BASED GRAPH CLUSTERING

Abstract

Software architecture recovery is to gain the architectural level understanding of a software system while its architecture description does not exist. In recent years, researchers have adopted various software clustering techniques to detect hierarchical structure of software systems. Most graph clustering techniques focus on the connectivity between program elements, but unreasonably ignore the similarity which is also a key measure for finding elements of one module. In this paper we propose a novel hierarchy graph clustering algorithm DGHC, which considers both similarity and connectivity between program elements. During the transformation of program dependence graph edges representing similarity between elements are added. Then similar elements are grouped by density-based approaches. The alternative strategy is adopted to find groups of closely connected and similar elements. Meanwhile we adjust the contribution of connectivity and similarity by a flexible clustering algorithm based on short random walk model, which can obtain more structure information of software to find its multiple layers. Furthermore a new method called Multi-layer Propagation Gap is proposed to suggest stable layers of hierarchy clustering result as multiple layers of software system. Extensive experimental results illustrate the effectiveness and efficiency of DGHC in detecting hierarchy structure of software through comparison with various software clustering methods.