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@article{175775,
        author = {P.C. SANDHYA and Dr. I. NASRULLA},
        title = {A Hybrid Machine Learning Framework Using Random Forest and XGBoost for Software Bug Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4664-4667},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175775},
        abstract = {Code smells, indicating poor design or implementation choices, can harm software maintainability and increase bug-proneness. This study explores the significance of code smell metrics in prediction models for detecting bug-prone code modules. By incorporating smell-based metrics, we aim to enhance bug prediction accuracy. Using 14 open-source projects from the PROMISE repository, all written in Java, we trained models with metrics like F1-score, accuracy, precision, and recall. Classifiers like Naïve Bayes, Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, and k-Nearest Neighbor were applied. RF and SVM outperformed the other methods, delivering higher accuracy both within versions and across projects, proving their effectiveness in predicting buggy components.},
        keywords = {Code smell, source code, smell-aware, bugs classification.},
        month = {April},
        }