Software Module Clustering as a Multi-Objective Search Problem

Software module clustering is the problem of automatically organizing software units into modules to improve program structure. There has been a great deal of recent interest in search-based formulations of this problem in which module boundaries are identified by automated search, guided by a fitness function that captures the twin objectives of high cohesion and low coupling in a single-objective fitness function. This paper introduces two novel multi-objective formulations of the software module clustering problem, in which several different objectives (including cohesion and coupling) are represented separately. In order to evaluate the effectiveness of the multi-objective approach, a set of experiments was performed on 17 real-world module clustering problems. The results of this empirical study provide strong evidence to support the claim that the multi-objective approach produces significantly better solutions than the existing single-objective approach.

1 Introduction

Software module clustering is an important and challenging problem in software engineering. It is widely believed that a well-modularized software system is easier to develop and maintain [8], [27], [29]. Typically, a good module structure is regarded as one that has a high degree of cohesion and a low degree of coupling [8], [27], [29]. Sadly, as software evolves, its modular structure tends to degrade [17], necessitating a process of restructuring to regain the cognitive coherence of previous incarnations. This paper is concerned with automated techniques for suggesting software clusterings, delimiting boundaries between modules that maximize cohesion while minimizing coupling.