A Bedbug Optimization-Based Machine Learning Framework for Software Fault Prediction

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dc.authorid0000-0002-7208-3576
dc.authorid0000-0003-2639-8048
dc.authorid0000-0003-1570-875X
dc.authorid0000-0002-0354-9344
dc.authorid0000-0001-5202-6315
dc.contributor.authorArasteh, Bahman
dc.contributor.authorSefati, Seyed Salar
dc.contributor.authorPopovici, Eduard-Cristian
dc.contributor.authorInce, Ibrahim Furkan
dc.contributor.authorKiani, Farzad
dc.date.accessioned2026-01-31T15:09:05Z
dc.date.available2026-01-31T15:09:05Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractPredicting software faults and identifying defective modules is a significant challenge in developing reliable software products. Machine Learning (ML) approaches on the historical fault datasets are utilized to classify faulty software modules. The presence of irrelevant features within the training datasets undermines the accuracy and precision of the software prediction models. Consequently, selecting the most effective features for module classification constitutes an NP-hard problem. This research introduces the Binary Bedbug Optimization Algorithm (BBOA) to extract the most effective features of training datasets. The primary contribution lies in the development of a binary variant of the Bedbug Optimization Algorithm (BOA) designed to effectively select effective features and build a classifier for identifying faulty software modules using ANN, SVM, DT, and NB algorithms. The model's performance was evaluated using five standard real-world NASA datasets. The findings reveal that among the 21 features analyzed, features such as code complexity, lines of code, the total number of operands and operators, lines containing both code and comments, the total count of operators and operands, and the number of branch instructions play a critical role in predicting software faults. The proposed method achieved notable improvements, with increases of 5.97% in accuracy, 3.86% in precision, 2.37% in sensitivity (recall), and 3.06% in F1-score.
dc.description.sponsorshipEuropean Commission [101183162]; Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI [PN-IV-P8-8.1-PRE-HE-ORG-2024-0236]; National University of Science and Technology POLITEHNICA Bucharest through the 'PubArt' Programme
dc.description.sponsorshipThis work was supported by a grant from the European Commission, No.101183162 (ANTIDOTE project), and by the Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI, Project No. PN-IV-P8-8.1-PRE-HE-ORG-2024-0236, within PNCDI IV. Thearticle was funded by the National University of Science and Technology POLITEHNICA Bucharest through the 'PubArt' Programme.
dc.identifier.doi10.3390/math13213531
dc.identifier.issn2227-7390
dc.identifier.issue21
dc.identifier.scopus2-s2.0-105021581387
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org./10.3390/math13213531
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10818
dc.identifier.volume13
dc.identifier.wosWOS:001612830200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofMathematics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectfault prediction
dc.subjectbinary bedbug optimization algorithm
dc.subjectfeature selection
dc.subjectmachine learning
dc.titleA Bedbug Optimization-Based Machine Learning Framework for Software Fault Prediction
dc.typeArticle

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