NATURE-BASED PREDICTION MODEL OF BUG REPORTS BASED ON ENSEMBLE MACHINE LEARNING MODEL

Abstract

The rapid growth of software systems has led to an increase in the volume and complexity of bug reports, necessitating efficient and accurate prediction models to manage and address these reports effectively. This paper presents a Nature-Based Prediction Model of Bug Reports (NBPMBR) leveraging ensemble machine learning techniques to enhance the prediction accuracy and reliability of bug report classifications. By integrating various nature-inspired algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) within an ensemble framework, NBPMBR combines the strengths of these algorithms to improve performance.The model undergoes rigorous training on historical bug report data, using feature extraction methods to capture relevant information such as bug severity, priority, and textual descriptions. The ensemble approach ensures robustness by mitigating the weaknesses of individual algorithms, leading to a more balanced and accurate prediction outcome. Experimental results on benchmark datasets demonstrate that NBPMBR outperforms traditional machine learning models in terms of precision, recall, and overall prediction accuracyThis nature-based ensemble model not only advances the state-of-the-art in bug report prediction but also provides a scalable and adaptable solution for real-world software maintenance and quality assurance processes. By automating the classification and prioritization of bug reports, NBPMBR aids in the efficient allocation of resources, thereby improving the software development lifecycle and enhancing the overall quality of software products.