A Hybrid Evolutionary Fuzzy Ensemble Approach for Accurate Software Defect Prediction

Raghunath Dey , Jayashree Piri , Biswaranjan Acharya , Pragyan Paramita Das , Vassilis C. Gerogiannis , Andreas Kanavos ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.20944/preprints202503.0259.v1

Software defect prediction aims to identify defect-prone modules before testing, reducing costs and duration. Machine learning (ML) techniques are widely used to develop predictive models for classifying defective software components. However, high-dimensional training datasets often degrade classification accuracy and precision due to irrelevant or redundant features. To address this, effective feature selection is crucial, but it poses an NP-hard challenge that can be efficiently tackled using heuristic algorithms. This study introduces a Binary Multi-Objective Starfish Optimizer (BMOSFO) for optimal feature selection, enhancing classification accuracy and precision. The proposed BMOSFO balances two conflicting objectives: minimizing the number of selected features and maximizing classification performance. A Choquet Fuzzy Integral-based Ensemble Classifier is then employed to further enhance prediction reliability by aggregating multiple classifiers. The effectiveness of the proposed approach is validated using five real-world NASA benchmark datasets, demonstrating superior performance compared to traditional classifiers. Experimental results reveal that key software metrics—such as design complexity, operators and operands count, lines of code, and number of branches—significantly influence defect prediction. The findings confirm that BMOSFO not only reduces feature dimensionality but also enhances classification performance, providing a robust and interpretable solution for software defect prediction. This approach shows strong potential for generalization to other high-dimensional classification tasks.