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Majority Vote Feature Selection Algorithm in Software Fault Prediction

Emin Borandag¹, Akin Ozcift¹, Deniz Kilinc¹ and Fatih Yucalar¹

1. Department of Software Engineering, Faculty of Technology
   Manisa Celal Bayar University, Manisa, Turkey
   {emin.borandag, akin.ozcift, deniz.kilinc, fatih.yucalar}@cbu.edu.tr

Abstract

Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance.

Key words

software fault prediction, majority voting, machine learning algorithm

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS180312039B

Publication information

Volume 16, Issue 2 (June 2019)
Year of Publication: 2019
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Borandag, E., Ozcift, A., Kilinc, D., Yucalar, F.: Majority Vote Feature Selection Algorithm in Software Fault Prediction. Computer Science and Information Systems, Vol. 16, No. 2, 515-539. (2019), https://doi.org/10.2298/CSIS180312039B