Performance Evaluation of Some Machine Learning Algorithms in NASA Defect Prediction Data Sets

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dc.contributor.authorAydın, Zeynep Behrin Güven
dc.contributor.authorSamli, Ruya
dc.date.accessioned2024-07-12T21:40:40Z
dc.date.available2024-07-12T21:40:40Z
dc.date.issued2020en_US
dc.department[Belirlenecek]en_US
dc.description5th International Conference on Computer Science and Engineering (UBMK) -- SEP 09-11, 2020 -- Diyarbakir, TURKEYen_US
dc.description.abstractThe main purpose of machine learning is to model the systems making predictions by using some mathematical and operational features on the data with computers [1].Today, there are many studies on machine learning in all areas of the software world. Software Defect Prediction is a sub-branch that progresses rapidly in machine learning. In this study, five of machine learning classification algorithms were conducted on with PYTHON programming language on defect prediction data sets which are JM1, KC1, CM1, PC1 in the PROMISE repository. These data sets are created within the scope of the publicly available NASA institution's Metric Data Program. The accuracy, recall, precision and F-measure and support values of the algorithms on the data are compared. When the results are examined in terms of the accuracy of machine learning algorithms, the accuracy rates of the algorithms are quite high in all 4 data sets. The highest success rates were obtained from the classification algorithms applied in 4 data sets in CM1 and PC1 data sets. In 4 data sets, the highest success rates were seen with Random Forest algorithm.en_US
dc.description.sponsorshipIEEE Turkey Sect,Istanbul Teknik Univ,Gazi Univ,Atilim Univ,Dicle Univ,Turkiye Bilisim Vakfi,Kocaeli Univen_US
dc.identifier.doi10.1109/ubmk50275.2020.9219531
dc.identifier.endpage297en_US
dc.identifier.isbn978-1-7281-7565-2
dc.identifier.scopus2-s2.0-85095697317en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage295en_US
dc.identifier.urihttps://doi.org/10.1109/ubmk50275.2020.9219531
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7435
dc.identifier.wosWOS:000629055500057en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2020 5th International Conference on Computer Science And Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY08779
dc.subjectSoftware Defect Predictionen_US
dc.subjectMccabeen_US
dc.subjectHalsteaden_US
dc.subjectRandom Foresten_US
dc.subjectAdaboosten_US
dc.subjectNaive Bayesen_US
dc.subjectDecision Treeen_US
dc.titlePerformance Evaluation of Some Machine Learning Algorithms in NASA Defect Prediction Data Setsen_US
dc.typeConference Object
dspace.entity.typePublication

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