Initial setting adjustment program software#
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The average MQ of the generated clusters for the selected benchmark set by BWO, PSO and TLB are 3.155, 3.120 and 2.778, respectively. The results of conducted experiments on the eight standard and real-world applications indicate that performance of the BWO, PSO, and TLB algorithms are higher than the other algorithms in SMC problem also, the performance of these algorithm increased when their initial population were generated with logistic chaos method instead of random method. Also, the effects of chaos theory in the performance of these algorithms in this problem have been experimentally investigated.
In this paper, five different heuristic algorithms (Bat, Cuckoo, Teaching–Learning-Based, Black Widow and Grasshopper algorithms) are proposed for optimal clustering of software modules. Poor success rate, low stability and modularization quality are regarded as the major drawbacks of the previously proposed methods. Minimizing the connections among the created clusters, maximizing the internal connections within the created clusters and maximizing the clustering quality are considered to be the most important objectives in software module clustering (SMC). Finding the best clustering model of a software system is regarded as a NP-complete problem. Clustering software modules, as a reverse engineering technique, is assumed to be an effective technique in extracting comprehensible structural-models of software from the source code. Comprehension of the structure of software will facilitate maintaining the software more efficiently.
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