Software defect prediction using wrapper feature selection based on dynamic re-reranking strategy

Abstract

Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.