A Hybrid Sampling and Multi-Objective Optimization Approach for Enhanced Software Defect Prediction

TL;DR

This paper presents a novel framework combining hybrid sampling and multi-objective optimization to improve software defect prediction accuracy, addressing class imbalance and feature selection challenges effectively.

Contribution

It introduces a new SDP method integrating hybrid sampling with multi-objective optimization and feature fusion, enhancing prediction accuracy and computational efficiency.

Findings

Improved data balance and feature selection in defect prediction

Enhanced prediction accuracy over existing methods

Robustness demonstrated across multiple datasets

Abstract

Accurate early prediction of software defects is essential to maintain software quality and reduce maintenance costs. However, the field of software defect prediction (SDP) faces challenges such as class imbalances, high-dimensional feature spaces, and suboptimal prediction accuracy. To mitigate these challenges, this paper introduces a novel SDP framework that integrates hybrid sampling techniques, specifically Borderline SMOTE and Tomek Links, with a suite of multi-objective optimization algorithms, including NSGA-II, MOPSO, and MODE. The proposed model applies feature fusion through multi-objective optimization, enhancing both the generalization capability and stability of the predictions. Furthermore, the integration of parallel processing for these optimization algorithms significantly boosts the computational efficiency of the model. Comprehensive experiments conducted on datasets from NASA and PROMISE repositories demonstrate that the proposed hybrid sampling and multi-objective optimization approach improves data balance, eliminates redundant features, and enhances prediction accuracy. The experimental results also highlight the robustness of the feature fusion approach, confirming its superiority over existing state-of-the-art techniques in terms of predictive performance and applicability across diverse datasets.