Title: A software fault prediction model using driving training-inspired metaheuristic feature selection framework

Authors: Himansu Das; Pratiti Mishra; Sanat Kumar Patro; Sushruta Mishra; Mahendra Kumar Gourisaria; Saurabh Bilgaiyan

Addresses: School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, 751024, Odisha, India ' Department of MCA, Trident Academy of Technology, Bhubaneswar, 751024, Odisha, India ' Department of Electrical and Electronics Engineering, Roland Institute of Technology, Berhampur, Odisha, India ' School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, 751024, Odisha, India ' School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, 751024, Odisha, India ' School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, 751024, Odisha, India

Abstract: In machine learning-based SFP, software metrics and defect data are utilised as independent features, and the datasets typically have high dimensionality. To reduce dimensionality and improve model performance, an effective feature selection (FS) approach is necessary. This study proposes a new FS method called driving training-based optimisation (FSDTBO). Unlike existing FS methods, the proposed algorithm maintains a healthy balance between exploration and exploitation, thereby identifying the most relevant features for SFP. The performance of FSDTBO was compared with four widely used FS methods: FSACO, FSGA, FSDE, and FSPSO. Four classifiers - NB, KNN, QDA, and DT - were employed for evaluation. Experimental results demonstrate that FSDTBO achieves higher classification accuracy and selects a more optimal subset of features compared to the other methods. Moreover, statistical analysis further confirms the superiority of the proposed approach over existing FS algorithms.

Keywords: FS; wrapper-based approach; software fault prediction; SFP; classifiers; driving training-based optimisation.

DOI: 10.1504/IJCSE.2026.155101

International Journal of Computational Science and Engineering, 2026 Vol.29 No.4, pp.426 - 444

Received: 06 Oct 2025
Accepted: 01 Mar 2026
Published online: 27 Jul 2026 *

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