Software Fault Prediction in Object Oriented Software Systems Using Ensemble Classifiers

dc.contributor.authorEmin BORANDAĞ
dc.contributor.authorFatih YÜCALAR
dc.contributor.authorKamil AKARSU
dc.date.accessioned2024-07-24T09:13:31Z
dc.date.available2024-07-24T09:13:31Z
dc.date.issued2018
dc.description.abstractThe main aim of software projects is developing software programs to meet functional and non-functional requirements within the project budget and at a particular time. The greatest challenge in reaching this goal is the software errors that were found in the software projects. The most basic technique that is used to solve software errors is testing the software programs according to the methods in the literature. These methods are the software tests that are basically conducted by software developers, although they have different methods of verification and validation according to their size, experience, techniques or tools they use. When software istested, it is very significant that software errors are found in the early phases. Software error estimation is a proven method of effectiveness and validity that increases the quality of software and reduces the cost of software development. In this study, by using machine learning algorithms and software metrics; software error estimation has been carried out with adeveloped software.
dc.identifier.DOI-ID10.18466/cbayarfbe.424521
dc.identifier.issn1305-130X
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/25345
dc.language.isoeng
dc.subject[Fen > Mühendislik > Bilgisayar Bilimleri, Yazılım Mühendisliği]
dc.titleSoftware Fault Prediction in Object Oriented Software Systems Using Ensemble Classifiers
dc.typeAraştırma Makalesi

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