Software Fault Prediction in Object Oriented Software Systems Using Ensemble Classifiers

Year 2018, , 297 - 302, 30.09.2018

Emin Borandağ , Fatih Yücalar , Kamil Akarsu

https://doi.org/10.18466/cbayarfbe.424521

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

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