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Software module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and structural information. The objective of clustering software modules is to achieve minimum coupling among different clusters and create maximum cohesion among the modules of each cluster. Finding the best clustering is considered to be a multi-objective N-P hard optimization-problem, and for solving this problem, different meta-heuristic algorithms have been previously proposed. Achieving higher module lustering quality (MQ), obtaining higher success rate for achieving the best clustering quality and improving convergence speed are the main objectives of this study.

In this study, a method (Bölen) is proposed for clustering software modules which combines the two algorithms of shuffled frog leaping and genetic algorithm.

The results of conducted experiments using traditional data sets confirm that the proposed method outperforms the previous methods in terms of convergence speed, module clustering quality and stability of the results.

The study proposes SFLA_GA algorithm for optimizing software module clustering, implementing SFLA algorithm in a discrete form by two operators of the genetic algorithm and achieving the above-mentioned purposes in this study. The aim is to achieve higher performance of the proposed algorithm in comparison with other algorithms.