Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability

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dc.authoridBorandag, Emin / 0000-0001-5553-2707
dc.authorscopusid57063539900
dc.authorscopusid25654077600
dc.authorscopusid57063310500
dc.authorscopusid56952927700
dc.authorwosidBorandag, Emin/L-1714-2019
dc.contributor.authorYücalar, Fatih
dc.contributor.authorÖzçift, Akın
dc.contributor.authorBorandağ, Emin
dc.contributor.authorKılınç, Deniz
dc.date.accessioned2022-02-15T16:59:03Z
dc.date.available2022-02-15T16:59:03Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractSoftware development projects require a critical and costly testing phase to investigate efficiency of the resultant product. As the size and complexity of project increases, manual prediction of software defects becomes a time consuming and costly task. An alternative to manual defect prediction is the use of automated predictors to focus on faulty modules and let the software engineer to examine the defective part with more detail. In this aspect, improved fault predictors will always find a software quality application project to be applied on. There are many base predictors tested-designed for this purpose. However, base predictors might be combined with an ensemble strategy to further improve to increase their performance, particularly fault-detection abilities. The aim of this study is to demonstrate fault-prediction performance of ten ensemble predictors compared to baseline predictors empirically. In our experiments, we used 15 software projects from PROMISE repository and we evaluated the fault-detection performance of algorithms in terms of F-measure (FM) and Area under the Receiver Operating Characteristics (ROC) Curve (AUC). The results of experiments demonstrated that ensemble predictors might improve fault detection performance to some extent. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.en_US
dc.identifier.doi10.1016/j.jestch.2019.10.005
dc.identifier.endpage950en_US
dc.identifier.issn2215-0986
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85075425550en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage938en_US
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2019.10.005
dc.identifier.urihttps://hdl.handle.net/20.500.14034/506
dc.identifier.volume23en_US
dc.identifier.wosWOS:000558754000009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltden_US
dc.relation.journalEngineering Science And Technology-An International Journal-Jestechen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjectEnsemble learningen_US
dc.subjectSoftware fault predictionen_US
dc.subjectSoftware quality engineeringen_US
dc.subjectStatic Code Attributesen_US
dc.subjectDefect Predictorsen_US
dc.subjectEnsembleen_US
dc.titleMultiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction abilityen_US
dc.typeArticleen_US

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