Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.

Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 

Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

Reference:

DOI: 10.36244/ICJ.2026.1.1

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Please cite this paper the following way:

Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J., "Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction ", Infocommunications Journal, Vol. XVIII, No 1, March 2026, pp. 2-10., https://doi.org/10.36244/ICJ.2026.1.1