A feature selection model for software defect prediction using binary Rao optimization algorithm

Abstract

In this digital world, using software has become an important part of daily life and business. The software must be rigorously tested in order to avert a financial crisis. The defect-free software enhances the functionality of the business. Predicting software defects in advance is a crucial task in the software industry. The aim of Software Defect Prediction (SDP) is to locate the possible software bugs. This paper proposes a hybrid feature selection (filter–wrapper) approach based on the multi-criteria decision making (MCDM) method and the Rao optimization method for selecting the more informative features to improve the software defect prediction rate. The proposed work measures the fitness of the candidate solution by using the defect prediction rate and the feature selection ratio. The performance of the proposed method is evaluated using three popular benchmark NASA datasets (PC5, JM1, and KC1) and compared with the state-of-the-art methods. The proposed feature subset selection scheme identifies the most significant feature subset for defect prediction with an average accuracy of 95% on the benchmark datasets. According to the experimental results, the proposed hybrid approach outperforms the standard strategy in terms of defect prediction rate.