Purpose

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.

Design/methodology/approach

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

Findings

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.

Originality/value

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.

中文翻译：

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Bölen：结合改组蛙跳和遗传算法的软件模块聚类方法

目的

软件模块集群是逆向工程技术之一，被认为是呈现软件体系结构和结构信息的有效技术。群集软件模块的目的是在不同群集之间实现最小的耦合，并在每个群集的模块之间创建最大的内聚力。找到最佳聚类被认为是一个多目标的N - P硬优化问题，并且为了解决该问题，先前已经提出了不同的元启发式算法。获得更高的模块着色质量（MQ），获得更高的成功率以实现最佳的聚类质量和提高收敛速度是本研究的主要目标。

设计/方法/方法

在这项研究中，提出了一种将软件模块进行聚类的方法（Bölen），该方法将洗牌蛙跳和遗传算法这两种算法结合在一起。

发现

使用传统数据集进行的实验结果证实，该方法在收敛速度，模块聚类质量和结果稳定性方面均优于以前的方法。

创意/价值

该研究提出了SFLA_GA算法，用于优化软件模块的聚类，由遗传算法的两个算子以离散形式实现SFLA算法，并达到了上述目的。目的是与其他算法相比，实现所提出算法的更高性能。