A novel multi-view ordinal classification approach for software bug prediction

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dc.authoridYILDIRIM TASER, Pelin/0000-0002-5767-2700
dc.authorwosidYILDIRIM TASER, Pelin/O-6422-2019
dc.contributor.authorTaşer, Pelin Yıldırım
dc.date.accessioned2023-03-22T19:47:19Z
dc.date.available2023-03-22T19:47:19Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractSoftware bug prediction aims to enhance software quality and testing efficiency by constructing predictive classification models using code properties. This enables the prompt detection of fault-prone modules. There are several machine learning-based software bug prediction studies, which mainly focus on single view data by disregarding the natural ordering relation among the class labels in the literature. Thus, these studies cause losing each view's own intrinsic structure and the inherent order of the labels that positively affect the prediction performance. To overcome this drawback, this study focuses on integrating ordering information and a multi-view learning strategy. This paper proposes a novel approach multi-view ordinal classification (MVOC), which learns from different views (complexity, coupling, cohesion, inheritance and scale) of the software dataset separately and predicts software bugs taking the inherent order of class labels (non-buggy, less buggy and more buggy) into consideration. To demonstrate its prediction performance, the MVOC approach was executed on the 40 different real-world software datasets using six different classification algorithms as base learners. In the experiments, the MVOC approach was compared with traditional classifiers and their multi-view implementations in terms of precision, recall, f-measure and accuracy rate metrics. The results indicate that the MVOC approach presents better prediction performance on average than the multi-view-based and traditional classifiers. It is also observed from the results that the MVOC.RF model achieved the highest classification performance with an average accuracy rate of 85.65%.en_US
dc.identifier.doi10.1111/exsy.13044
dc.identifier.issn0266-4720
dc.identifier.issn1468-0394
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85130628729en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1111/exsy.13044
dc.identifier.urihttps://hdl.handle.net/20.500.14034/620
dc.identifier.volume39en_US
dc.identifier.wosWOS:000800288300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.journalExpert Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectmachine learningen_US
dc.subjectmulti-view learningen_US
dc.subjectordinal classificationen_US
dc.subjectsoftware bug predictionen_US
dc.subjectsoftware engineeringen_US
dc.titleA novel multi-view ordinal classification approach for software bug predictionen_US
dc.typeArticleen_US

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