A Hybrid Evolutionary Fuzzy Ensemble Approach for Accurate Software Defect Prediction

Raghunath Dey, Jayashree Piri, Biswaranjan Acharya (), Pragyan Paramita Das, Vassilis C. Gerogiannis () and Andreas Kanavos
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Raghunath Dey: School of Computer Engineering, KIIT University, Bhubaneswar 751024, Odisha, India
Jayashree Piri: Department of CSE, Silicon University, Bhubaneswar 751024, Odisha, India
Biswaranjan Acharya: Department of Computer Engineering AI, Marwadi University, Rajkot 360003, Gujarat, India
Pragyan Paramita Das: Department of CSE, Silicon University, Bhubaneswar 751024, Odisha, India
Vassilis C. Gerogiannis: Department of Digital Systems, University of Thessaly, 382 21 Larissa, Greece
Andreas Kanavos: Department of Informatics, Ionian University, 491 00 Corfu, Greece

Mathematics, 2025, vol. 13, issue 7, 1-34

Abstract: Software defect prediction identifies defect-prone modules before testing, reducing costs and development time. Machine learning techniques are widely used, but high-dimensional datasets often degrade classification accuracy due to irrelevant features. To address this, effective feature selection is essential but remains an NP-hard challenge best tackled with heuristic algorithms. This study introduces a binary, multi-objective starfish optimizer for optimal feature selection, balancing feature reduction and classification performance. A Choquet fuzzy integral-based ensemble classifier further enhances prediction reliability by aggregating multiple classifiers. The approach was validated on five NASA datasets, demonstrating superior performance over traditional classifiers. Key software metrics—such as design complexity, operators and operands count, lines of code, and numbers of branches—were found to significantly influence defect prediction. The results show that the proposed method improves classification performance by 1% to 13% while retaining only 33% to 57% of the original feature set, offering a reliable and interpretable solution for software defect prediction. This approach holds strong potential for broader, high-dimensional classification tasks.

Keywords: software defect prediction; feature selection; binary multi-objective optimization; starfish optimizer; fuzzy ensemble learning; choquet fuzzy integral (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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