Hyper-heuristic approach for multi-objective software module clustering

Abstract

In the software maintenance phase of software development life cycle, one of the main concerns of software engineers is to group the modules into clusters with maximum cohesion and minimum coupling.To analyze the efficacy of Multi-objective Hyper-heuristic Evolutionary Algorithm (MHypEA) in solving real-world clustering problems and to compare the results with the reported results in the literature for single as well as multi-objective formulations of the problem and also to present a CASE tool that assists software engineers in software module clustering process.The paper reports on empirical evaluation of the performance of MHypEA with the reported results in the literature. The comparison is mainly based on two factors – quality of the obtained solutions and the computational effort.On all the attempted problems, MHypEA reported good results in comparison to all the studies that were reported on multi-objective formulation of the problem, with a computational effort of nearly one-twentieth of the computational effort required by the other multi-objective algorithms.The hyper-heuristic approach is able to produce high quality clustered systems with less computational effort.