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The impact of feature types, classifiers, and data balancing techniques on software vulnerability prediction models

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dc.contributor.author Kaya, Aydın
dc.contributor.author Keçeli, Ali Seydi
dc.contributor.author Çatal, Çağatay
dc.contributor.author Tekinerdoğan, Bedir
dc.date.accessioned 2021-06-10T12:11:12Z
dc.date.available 2021-06-10T12:11:12Z
dc.date.issued 2019-09
dc.identifier.citation Kaya, Aydın...et al (2019). "The impact of feature types, classifiers, and data balancing techniques on software vulnerability prediction models", Journal of Software: Evolution and Process, Vol. 31, No. 9. tr_TR
dc.identifier.issn 2047-7473
dc.identifier.issn 2047-7481
dc.identifier.uri http://hdl.handle.net/20.500.12416/4762
dc.description.abstract Software vulnerabilities form an increasing security risk for software systems, that might be exploited to attack and harm the system. Some of the security vulnerabilities can be detected by static analysis tools and penetration testing, but usually, these suffer from relatively high false positive rates. Software vulnerability prediction (SVP) models can be used to categorize software components into vulnerable and neutral components before the software testing phase and likewise increase the efficiency and effectiveness of the overall verification process. The performance of a vulnerability prediction model is usually affected by the adopted classification algorithm, the adopted features, and data balancing approaches. In this study, we empirically investigate the effect of these factors on the performance of SVP models. Our experiments consist of four data balancing methods, seven classification algorithms, and three feature types. The experimental results show that data balancing methods are effective for highly unbalanced datasets, text-based features are more useful, and ensemble-based classifiers provide mostly better results. For smaller datasets, Random Forest algorithm provides the best performance and for the larger datasets, RusboostTree achieves better performance. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1002/smr.2164 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Classification Models tr_TR
dc.subject Data Sampling tr_TR
dc.subject Imbalance Datasets tr_TR
dc.subject Machine Learning tr_TR
dc.subject Performance Analysis tr_TR
dc.subject Software Vulnerability Prediction tr_TR
dc.title The impact of feature types, classifiers, and data balancing techniques on software vulnerability prediction models tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Software: Evolution and Process tr_TR
dc.contributor.authorID 3530 tr_TR
dc.identifier.volume 31 tr_TR
dc.identifier.issue 9 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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