A novel regression method for software defect prediction with kernel methods

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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.accessioned2019-08-31T12:10:23Z
dc.date.accessioned2019-08-05T16:04:57Z
dc.date.available2019-08-31T12:10:23Z
dc.date.available2019-08-05T16:04:57Z
dc.date.issued2013
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractIn this paper, we propose a novel method based on SVM to predict the number of defects in the files or classes of a software system. To model the relationship between source code similarity and defectiveness, we use SVM with a precomputed kernel matrix. Each value in the kernel matrix shows how much similarity exists between the files or classes of the software system tested. The experiments on 10 Promise datasets indicate that SVM with a precomputed kernel performs as good as the SVM with the usual linear or RBF kernels in terms of the root mean square error (RMSE). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of this study suggest that source code similarity is a good means of predicting the number of defects in software modules. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities.en_US
dc.description.sponsorshipInst. Syst. Technol. Inf., Control Commun. (INSTICC)en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationOkutan, A. & Yıldız, O. T. (2013). A novel regression method for software defect prediction with kernel methods. Paper present at the Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 216-221.en_US
dc.identifier.endpage221
dc.identifier.isbn9789898565419
dc.identifier.scopus2-s2.0-84877972792
dc.identifier.scopusqualityN/A
dc.identifier.startpage216
dc.identifier.urihttps://hdl.handle.net/11729/1919
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.relation.ispartofProceedings of the 2nd International Conference on Pattern Recognition Applications and Methodsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer softwareen_US
dc.subjectDefect predictionen_US
dc.subjectDefectsen_US
dc.subjectForecastingen_US
dc.subjectKernel methodsen_US
dc.subjectMean square erroren_US
dc.subjectPattern recognitionen_US
dc.subjectRegression analysisen_US
dc.subjectRegression methoden_US
dc.subjectRoot mean square errorsen_US
dc.subjectSoftware defectsen_US
dc.subjectSoftware engineeringen_US
dc.subjectSoftware defect predictionen_US
dc.subjectSoftware modulesen_US
dc.subjectSource code similaritiesen_US
dc.subjectSVMen_US
dc.titleA novel regression method for software defect prediction with kernel methodsen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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