INTELLIGENT BUG PREDICTION SYSTEM

Abstract

The intelligent bug prediction system addresses the main problem of identifying the defect-prone software modules in the early stages of the software development life cycle. The conventional testing methodologies are time-consuming and costly and do not provide an effective way to prioritise high-risk modules. This problem is important for improving software reliability, reducing maintenance cost and improving the overall quality of software systems. The current bug prediction techniques mainly rely on traditional machine learning models such as Random Forests, Support Vector Machines (SVMs), and Neural Networks, but they face challenges such as imbalanced data, limited feature sets, low interpretability, and binary predictions that do not provide meaningful guidance for testing prioritisation.In our approach, we propose an intelligent machine learning-based bug prediction framework that uses SMOTE for dataset balancing and feature selection to identify the most relevant software metrics. We also use advanced ensemble learning techniques, such as CatBoost, LightGBM, and the Stacking Ensemble model, to improve prediction accuracy. Methodology: Analysed software metrics from NASA MDP evaluated model performance using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix; and applied SHAP (SHapley Additive Explanations) to provide transparent and interpretable predictions. A Risk Scoring Mechanism categorises software modules as Stable, High, Medium, or Low risk, and Bug Fix Recommendations help developers more effectively fix predicted defects.The proposed system is expected to produce accurate, explainable, and risk-aware bug predictions. This enables developers to prioritise testing efforts, optimise resource allocation, cut debugging costs, and deliver more dependable, secure, and high-quality software.