Software defect prediction using Bayesian networks

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dc.authorid0000-0001-6664-515X
dc.authorid0000-0001-5838-4615
dc.contributor.authorOkutan, Ahmeten_US
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2015-01-15T23:02:52Z
dc.date.available2015-01-15T23:02:52Z
dc.date.issued2014-02
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractThere are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.en_US
dc.description.versionPublisher's Versionen_US
dc.description.versionAuthor Pre-Printen_US
dc.identifier.citationOkutan, A. & Yıldız, O. T. (2014). Software defect prediction using bayesian networks. Empirical Software Engineering, 19(1), 154-181. doi:10.1007/s10664-012-9218-8en_US
dc.identifier.doi10.1007/s10664-012-9218-8
dc.identifier.endpage181
dc.identifier.issn1382-3256
dc.identifier.issn1573-7616
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84893780047
dc.identifier.scopusqualityQ1
dc.identifier.startpage154
dc.identifier.urihttps://hdl.handle.net/11729/548
dc.identifier.urihttp://dx.doi.org/10.1007/s10664-012-9218-8
dc.identifier.volume19
dc.identifier.wosWOS:000330983900005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorOkutan, Ahmeten_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-6664-515X
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringeren_US
dc.relation.ispartofEmpirical Software Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDefect predictionen_US
dc.subjectBayesian networksen_US
dc.subjectObject-oriented designen_US
dc.subjectEmpirical-analysisen_US
dc.subjectFault predictionen_US
dc.subjectMetricsen_US
dc.subjectModelsen_US
dc.subjectReliabilityen_US
dc.titleSoftware defect prediction using Bayesian networksen_US
dc.typeArticleen_US
dspace.entity.typePublication

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