Software Fault Prediction in Object Oriented Software Systems Using Ensemble Classifiers

Authors : Emin BORANDAĞ, Fatih YÜCALAR, Kamil AKARSU

Pages : 297-302

Doi:10.18466/cbayarfbe.424521

View : 22 | Download : 21

Publication Date : 2018-09-30

Article Type : Research Paper

Abstract :The 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 is tested, 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 a developed software
Keywords : Data Mining, Software Fault Prediction, Rotation Forest Algorithm, Ensemble Learning