Multiple Feature Selection Frameworks Based on Evolutionary Computing and Ensemble Learning for Software Defect Prediction

Abstract

Software defects may cause severe crashes in the system, leading to the software's high maintenance costs. Early identification of these defects would lead to high-quality software, thus saving time and money. This study proposes five feature selection approaches based on evolutionary computing algorithms, each coupled with a majority voting ensemble for Software defect prediction. The objective is to improve the existing process by targeting the metric selection stage. The study was conducted on thirty open-source defect datasets. The proposed feature selection techniques were applied on a within-project defect prediction model and a heterogeneous defect prediction model. The Friedman and the Wilcoxon Signed-rank test concluded that the proposed techniques were promising and generated results comparable to some other state-of-the-art feature selection methodologies.