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dc.contributor.authorYüksel, U.
dc.contributor.authorSözer, Hasan
dc.contributor.authorŞensoy, Murat
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractStatic code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. The effectiveness of many different classifiers and artifact characteristics have been evaluated for this application domain. However, the effectiveness of classifier fusion methods have not been investigated yet. In this work, we evaluate several existing classifier fusion approaches in the context of an industrial case study to classify the alerts generated for a digital TV software. In addition, we employ a trust-based classifier fusion method. We observed that our approach can increase the accuracy of classification by up to 4%.
dc.description.sponsorshipVestel Electronics ; U.S. Army Research Laboratory ; TÜBİTAK
dc.relation.ispartofInformation Fusion (FUSION), 2014 17th International Conference on
dc.titleTrust-based fusion of classifiers for static code analysisen_US
dc.typeConference paperen_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-2968-4763 & YÖK ID 23178) Sözer, Hasan
dc.contributor.authorID(ORCID 0000-0001-8806-4508 & YÖK ID 41438) Şensoy, Murat
dc.contributor.ozuauthorSözer, Hasan
dc.contributor.ozuauthorŞensoy, Murat
dc.subject.keywordsClassifer fusion
dc.subject.keywordsTrust-based fusion
dc.subject.keywordsAlert classification
dc.subject.keywordsIndustrial case study
dc.subject.keywordsStatic code analysis
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff

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