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January 1, 2026International Journal of Computational Science and Engineering

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

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

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Key Points

The study aims to develop a software fault prediction model utilizing a feature selection framework inspired by driving training techniques.
Developed a software fault prediction model using a metaheuristic feature selection approach inspired by driving training concepts.
Conducted simulations to test the effectiveness of this model against traditional methods.
Assessed prediction accuracy and fault detection rates.
Demonstrated a significant improvement in prediction accuracy compared to conventional models, with an accuracy rate exceeding 85%.
Reported an increase in fault detection rates by 20%, indicating enhanced model reliability.

Implication

Evaluates a software fault prediction model using driving training methods, suggesting improved accuracy in predictions.

Abstract

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Cite This Study

Das et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff420d674f7c03778d490 https://doi.org/10.1504/ijcse.2026.10078586

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