A Hybrid Machine Learning Framework Using Random Forest and XGBoost for Software Bug Prediction

P.C. SANDHYA; Dr. I. NASRULLA

A Hybrid Machine Learning Framework Using Random Forest and XGBoost for Software Bug Prediction

Authors: P.C. SANDHYA, Dr. I. NASRULLA

Unique Paper ID: 175775
Volume: 11
Issue: 11
PageNo: 4664-4667

Keywords: Code smell source code smell-aware bugs classification.

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4664-4667

A Hybrid Machine Learning Framework Using Random Forest and XGBoost for Software Bug Prediction

Available:https://ijirt.org/Article?manuscript=175775

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