Single and multi-objective metaheuristic algorithms and their applications in software maintenance

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dc.authorscopusidBahman Arasteh / 39861139000
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.contributor.authorArasteh, Bahman
dc.contributor.authorSadegi, Razieh
dc.contributor.authorAghaei, Babak
dc.contributor.authorGhanbarzadeh, Reza
dc.date.accessioned2025-04-18T09:45:54Z
dc.date.available2025-04-18T09:45:54Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractThe comprehension of software structure plays a significant role in efficiently maintaining software. Clustering software modules has been regarded as a successful method for extracting understandable structural models from source code, among other reverse engineering techniques. However, the problem of achieving the optimal model for clustering is considered NP-complete. The primary objective of Software Module Clustering (SMC) is minimizing inter-cluster connections, maximizing intra-cluster connections, and improving clustering quality. Different optimization algorithms (Seyyedabbasi, 2023) have been used to sort out the optimization problems. The majority of proposed methods to address SMC problem have shown some drawbacks, such as a lower rate of success, stability, and quality of modularization. This chapter reviews and compares seven heuristic algorithms that can be employed to solve software module clustering, namely PSO, GA, PSO-GA, COA, GWO, SCSO and OOA, in terms of achieving optimal clustering of software modules. Through experiments conducted on 10 real-world standard applications, the findings demonstrate that OOA, GWO, and SCSO perform better than the other methods in handling SMC. Notably, when the initial population of these methods is generated by the use of the logistic chaos technique instead of the random technique, these algorithms perform much better compared to others. The average quality of the modularity of the clusters created by OOA, GWO, and SCSO for the selected benchmark set is 3.937, 3.120, 3.107, respectively. The findings present an exploration of heuristic algorithms for optimal SMC; therefore, the positive impact of chaos theory is highlighted. OOA, GWO, SCSO, and COA demonstrate promising results, indicating their potential for practical application in the field of software maintenance and comprehension. By addressing the drawbacks of the evaluated methods, this chapter contributes to the advancement of software clustering techniques and facilitates the creation of more maintainable and efficient software systems.
dc.identifier.citationArasteh, B., Sadegi, R., Aghaei, B., & Ghanbarzadeh, R. (2024). Single and multi-objective metaheuristic algorithms and their applications in software maintenance. Decision-Making Models, 97-110.
dc.identifier.doi10.1016/B978-0-443-16147-6.00010-4
dc.identifier.endpage110
dc.identifier.isbn978-044316147-6
dc.identifier.isbn978-044316148-3
dc.identifier.scopus2-s2.0-85202801169
dc.identifier.startpage97
dc.identifier.urihttp://dx.doi.org/10.1016/B978-0-443-16147-6.00010-4
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6854
dc.indekslendigikaynakScopus
dc.institutionauthorArasteh, Bahman
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofDecision-making models: a perspective of fuzzy logic and machine learning
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectModularization Quality
dc.subjectModule Dependency Graph
dc.subjectSingle and Multi-Objective Heuristic Algorithms
dc.subjectSoftware Maintenance
dc.subjectSoftware Module Clustering
dc.titleSingle and multi-objective metaheuristic algorithms and their applications in software maintenance
dc.typeBook Chapter

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