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

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.