Program source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithms

Basit Öğe Kaydı
dc.authoridBahman Arasteh / 0000-0001-5202-6315
dc.authorscopusidBahman Arasteh / 39861139000en_US
dc.authorwosidBahman Arasteh / AAN-9555-2021en_US
dc.contributor.authorArasteh, Bahman
dc.contributor.authorAbdi, Mohammad
dc.contributor.authorBouyer, Asgarali
dc.date.accessioned2022-11-04T07:56:25Z
dc.date.available2022-11-04T07:56:25Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractMaintenance 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.en_US
dc.identifier.citationArasteh, 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.2022.103252en_US
dc.identifier.doi10.1016/j.advengsoft.2022.103252en_US
dc.identifier.scopus2-s2.0-85137162135en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.advengsoft.2022.103252
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3221
dc.identifier.volume173en_US
dc.identifier.wosWOS:000857299100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArasteh, Bahman
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofAdvances in Engineering Softwareen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCohesionen_US
dc.subjectCouplingen_US
dc.subjectGray Wwolf Optimization Algorithmen_US
dc.subjectModularization Qualityen_US
dc.subjectSoftware Modules Custeringen_US
dc.subjectSource Code Comprehensioen_US
dc.titleProgram source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithmsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
1-s2.0-S0965997822001557-main.pdf
Boyut:
3.68 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu
Yazılım Mühendisliği Bölümü Makale Koleksiyonu