A software defect prediction method using binary gray wolf optimizer and machine learning algorithms

Basit Öğe Kaydı
dc.authorscopusidBahman Arasteh / 39861139000
dc.authorscopusidKeyvan Arasteh / 57220034945
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.authorwosidKeyvan Arasteh / EJM-7867-2022
dc.contributor.authorWang, Hao
dc.contributor.authorArasteh, Bahman
dc.contributor.authorArasteh, Keyvan
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorRouhi, Alireza
dc.date.accessioned2025-04-18T08:45:37Z
dc.date.available2025-04-18T08:45:37Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractContext: Software defect prediction means finding defect-prone modules before the testing process which will reduce testing cost and time. Machine learning methods can provide valuable models for developers to classify software faulty modules. Problem: The inherent problem of the classification is the large volume of the training dataset's features, which reduces the accuracy and precision of the classification results. The selection of the effective features of the training dataset for classification is an NP-hard problem that can be solved using heuristic algorithms. Method: In this study, a binary version of the Gray Wolf optimizer (bGWO) was developed to select the most effective features of the training dataset. By selecting the most influential features in the classification, the precision and accuracy of the software module classifiers can be increased. Contribution: Developing a binary version of the gray wolf optimization algorithm to optimally select the effective features and creating an effective defect predictor are the main contributions of this study. To evaluate the effectiveness of the proposed method, five real-world and standard datasets have been used for the training and testing stages of the classifier. Results: The results indicate that among the 21 features of the train datasets, the basic complexity, sum of operators and operands, lines of codes, number of lines containing code and comments, and sum of operands have the greatest effect in predicting software defects. In this research, by combining the bGWO method and machine learning algorithms, accuracy, precision, recall, and F1 criteria have been considerably increased.
dc.identifier.citationWang, H., Arasteh, B., Arasteh, K., Gharehchopogh, F. S., & Rouhi, A. (2024). A software defect prediction method using binary gray wolf optimizer and machine learning algorithms. Computers and Electrical Engineering, 118, 109336.
dc.identifier.doi10.1016/j.compeleceng.2024.109336
dc.identifier.endpage30
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85194374947
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.compeleceng.2024.109336
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6610
dc.identifier.volume118
dc.identifier.wosWOS:001248544800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArasteh, Bahman
dc.institutionauthorArasteh, Keyvan
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.institutionauthoridKeyvan Arasteh / 0000-0002-2041-6439
dc.language.isoen
dc.publisherPergamon-Elsevier Science
dc.relation.ispartofComputers and electrical engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectSoftware Defect Prediction
dc.subjectFeature Selection
dc.subjectBinary Gray Wolf Optimizer
dc.subjectMachine Learning
dc.subjectModule Classification
dc.titleA software defect prediction method using binary gray wolf optimizer and machine learning algorithms
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Yazılım Mühendisliği Bölümü Makale Koleksiyonu