Reconstructing Software High-Level Architecture by Clustering Weighted Directed Class Graph

Abstract

Software architecture reconstruction plays an important role in software reuse, evolution and maintenance. Clustering is a promising technique for software architecture reconstruction. However, the representation of software, which serves as clustering input, and the clustering algorithm need to be improved in real applications. The representation should contain appropriate and adequate information of software. Furthermore, the clustering algorithm should be adapted to the particular demands of software architecture reconstruction well. In this paper, we first extract Weighted Directed Class Graph (WDCG) to represent object-oriented software. WDCG is a structural and quantitative representation of software, which contains not only the static information of software source code but also the dynamic information of software execution. Then we propose a WDCG-based Clustering Algorithm (WDCG-CA) to reconstruct high-level software architecture. WDCG-CA makes full use of the structural and quantitative information of WDCG, and avoids wrong compositions and arbitrary partitions successfully in the process of reconstructing software architecture. We introduce four metrics to evaluate the performance of WDCG-CA. The results of the comparative experiments show that WDCG-CA outperforms the comparative approaches in most cases in terms of the four metrics.