Software maintenance severity prediction with soft computing approach

Basit Öğe Kaydı
dc.authorscopusid36130978500
dc.authorscopusid36348997600
dc.authorscopusid23470640800
dc.contributor.authorArdil E.
dc.contributor.authorUçar E.
dc.contributor.authorSandhu P.S.
dc.date.accessioned2024-06-12T10:29:30Z
dc.date.available2024-06-12T10:29:30Z
dc.date.issued2009
dc.description.abstractAs the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA's public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software. © 2009 WASET.ORG.en_US
dc.identifier.endpage144en_US
dc.identifier.issn2010-376X
dc.identifier.scopus2-s2.0-77953728251en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage139en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14551/17765
dc.identifier.volume38en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofWorld Academy of Science, Engineering and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAccuracy; Fuzzy; Mae; Neuro-Fuzzy; Rmse; Software Faults; Software Metricsen_US
dc.subjectAccuracy; Fuzzy; Mae; Neuro-Fuzzy; Rmse; Software Faults; Software Metrics; Computer Programming; Computer Software; Fuzzy Inference; Learning Algorithms; Learning Systems; Nasa; Soft Computing; Computer Software Maintenanceen_US
dc.titleSoftware maintenance severity prediction with soft computing approachen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu