Enhancing software fault prediction using wrapper-based metaheuristic feature selection methods

The application of software fault prediction (SFP) to predict faulty components at the early stage has been investigated in various studies. Reducing feature redundancy is key to enhancing the predictive accuracy of SFP models. Feature selection methods are utilized to select and retain the features that contribute the most information while eliminating irrelevant or redundant features from software fault datasets. However, feature selection (FS) in the field of SFP remains a broad and continuously evolving field, encompassing a diverse range of techniques and methodologies. In this work, we study and perform empirical evaluation of ten wrapper FS methods, namely artificial butterfly optimization (ABO), atom search optimization (ASO), equilibrium optimizer (EO), Henry gas solubility optimization (HGSO), poor and rich optimization (PRO), generalized normal distribution optimization (GNDO), slime mold algorithm, Harris hawk’s optimization, pathfinder algorithm (PFA) and manta ray foraging optimization for resolving the data redundancy issue in SFP datasets. Experimental results on nine fault datasets from the PROMISE and AEEEM repositories show that the EO achieves the best performance, with PRO and HGSO ranking next. The comparative analysis revealed that ten wrapper-based FS methods demonstrated a substantial improvement in handling data redundancy issues for SFP.