The Effect of Environmental Metrics on Software Fault Prediction

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dc.authoridGül, Ensar/0000-0001-8753-6075en_US
dc.contributor.authorOdabaşı, Merve
dc.contributor.authorGül, Ensar
dc.date.accessioned2024-07-12T21:37:33Z
dc.date.available2024-07-12T21:37:33Z
dc.date.issued2023en_US
dc.department[Belirlenecek]en_US
dc.description.abstractIn this study, besides the software metrics, the environmental metrics such as experience of software engineer, similar project experience, size of the project, programming language, time spent on analysis and development are also explored to see whether they also affect the results of software fault prediction and what would be the success rates. The dataset for this study was generated from combining various data from 10 projects. A total of 36 metrics and 6676 test cases were evaluated. The errors occurred in the test cases are not just considered as an error, their priority and cases that cannot be tested are also taken into consideration. Nine fault levels are employed in models. Models are created with four different algorithms which have achieved a success rate of; 76% by the decision tree algorithm, 94% by the nearest neighbors algorithm, 90% by the random forests algorithm and 73% by the Adaboost Classifier Algorithm. It was observed that environmental metrics are indeed effective in software fault prediction and when applied with machine learning algorithms a high rate of success can be achieved.en_US
dc.identifier.doi10.1142/S021819402250067X
dc.identifier.endpage108en_US
dc.identifier.issn0218-1940
dc.identifier.issn1793-6403
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85144482014en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage85en_US
dc.identifier.urihttps://doi.org/10.1142/S021819402250067X
dc.identifier.urihttps://hdl.handle.net/20.500.12415/6845
dc.identifier.volume33en_US
dc.identifier.wosWOS:000898127400001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherWorld Scientific Publ Co Pte Ltden_US
dc.relation.ispartofInternational Journal of Software Engineering And Knowledge Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY04187
dc.subjectSoftware Testingen_US
dc.subjectSoftware Fault Predictionen_US
dc.subjectEnvironmental Metricsen_US
dc.subjectMachine Learningen_US
dc.titleThe Effect of Environmental Metrics on Software Fault Predictionen_US
dc.typeArticle
dspace.entity.typePublication

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