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

Putting the Developer in-the-Loop: An Interactive GA for Software Re-modularization

Abstract. This paper proposes the use of Interactive Genetic Algo-rithms (IGAs) to integrate developer’s knowledge in a re-modularization task. Specifically, the proposed algorithm uses a fitness composed of automatically-evaluated factors—accounting for the modularization qual-ity achieved by the solution—and a human-evaluated factor, penalizing cases where the way re-modularization places components into modules is considered meaningless by the developer. The proposed approach has been evaluated to re-modularize two software systems, SMOS and GESA. The obtained results indicate that IGA is able to produce solutions that, from a developer’s perspective, are more meaningful than those generated using the full-automated GA. While keeping feedback into account, the approach does not sacrifice the mod-ularization quality, and may work requiring a very limited set of feedback only, thus allowing its application also for large systems without requir-ing a substantial human effort.