Software Fault Prediction in Object Oriented Software Systems Using Ensemble Classifiers | Zendy

Emin Borandağ | Zendy; Fatih Yücalar | Zendy; Kamil Akarsu | Zendy

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Software Fault Prediction in Object Oriented Software Systems Using Ensemble Classifiers

Author(s) -

Emin Borandağ,

Fatih Yücalar,

Kamil Akarsu

Publication year - 2018

Publication title -

celal bayar üniversitesi fen bilimleri dergisi

Language(s) - English

Resource type - Journals

eISSN - 1305-1385

pISSN - 1305-130X

DOI - 10.18466/cbayarfbe.424521

Subject(s) - software sizing , software construction , verification and validation , computer science , software metric , software development , software reliability testing , software quality , software , software engineering , software verification , software measurement , software system , personal software process , reliability engineering , programming language , engineering , statistics , mathematics

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

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