Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11250
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dc.contributor.authorYılmaz, Rahime-
dc.contributor.authorNalçakan, Yağız-
dc.contributor.authorHaktanır, Elif-
dc.date.accessioned2021-11-06T09:27:12Z-
dc.date.available2021-11-06T09:27:12Z-
dc.date.issued2022-
dc.identifier.isbn9783030855765-
dc.identifier.issn2367-3370-
dc.identifier.urihttp://doi.org/10.1007/978-3-030-85577-2_48-
dc.identifier.urihttps://hdl.handle.net/11147/11250-
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021en_US
dc.description.abstractResearchers have successfully implemented machine learning classifiers to predict bugs in a change file for years. Change classification focuses on determining if a new software change is clean or buggy. In the literature, several bug prediction methods at change level have been proposed to improve software reliability. This paper proposes a model for classification-based bug prediction model. Four supervised machine learning classifiers (Support Vector Machine, Decision Tree, Random Forrest, and Naive Bayes) are applied to predict the bugs in software changes, and performance of these four classifiers are characterized. We considered a public dataset and downloaded the corresponding source code and its metrics. Thereafter, we produced new software metrics by analyzing source code at class level and unified these metrics with the existing set. We obtained new dataset to apply machine learning algorithms and compared the bug prediction accuracy of the newly defined metrics. Results showed that our merged dataset is practical for bug prediction based experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBug predictionen_US
dc.subjectClassificationen_US
dc.subjectCode analysisen_US
dc.subjectCode metricsen_US
dc.subjectMachine learningen_US
dc.subjectSoftware metricsen_US
dc.titleA novel feature to predict buggy changes in a software systemen_US
dc.typeConference Objecten_US
dc.institutionauthorNalçakan, Yağız-
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume308en_US
dc.identifier.startpage407en_US
dc.identifier.endpage414en_US
dc.identifier.scopus2-s2.0-85115224913en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/978-3-030-85577-2_48-
dc.identifier.scopusqualityQ4-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextnone-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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