Program source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithms
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Maintenance is a critical and costly phase of software lifecycle. Understanding the structure of software will make it much easier to maintain the software. Clustering the modules of software is regarded as a useful reverse engineering technique for constructing software structural models from source code. Minimizing the connections between produced clusters, maximizing the internal connections within the clusters, and maximizing the clustering quality are the most important objectives in software module clustering. Finding the optimal software clustering model is regarded as an NP-complete problem. The low success rate, limited stability, and poor modularization quality are the main drawbacks of the previous methods. In this paper, a combination of gray wolf optimization algorithm and genetic algorithms is suggested for efficient clustering of software modules. An extensive series of experiments on 14 standard benchmarks have been conducted to evaluated the proposed method. The results illustrate that using the combination of gray wolf and genetic algorithms to the software-module clustering problem increases the quality of clustering. In terms of modularization quality and convergence speed, proposed hybrid method outperforms the other heuristic approaches.
Açıklama
Anahtar Kelimeler
Cohesion, Coupling, Gray Wwolf Optimization Algorithm, Modularization Quality, Software Modules Custering, Source Code Comprehensio
Kaynak
Advances in Engineering Software
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
173
Sayı
Künye
Arasteh, B., Abdi, M., & Bouyer, A. (2022). Program source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithms. Advances in Engineering Software, 173 doi:10.1016/j.advengsoft.2022.103252
