Enhancing Software Reliability through Intelligent Fault Prediction Using Machine Learning

Abstract

Software fault prediction plays a crucial role in enhancing software quality and reliability by enabling early detection of defect-prone modules. This study proposes a machine learning-based predictive framework to identify software faults using historical software metrics and defect data. The objective is to evaluate and compare the performance of various machine learning algorithms—Random Forest, XGBoost, Support Vector Machine (SVM), and Neural Networks—for their effectiveness in fault detection. Public benchmark datasets comprising object-oriented metrics such as CK, Halstead, and McCabe were used after undergoing pre-processing steps including normalization; class balancing using SMOTE, and feature selection through PCA and RFE. Models were trained and tested using stratified k-fold cross-validation, and evaluated using metrics such as Accuracy, Precision, Recall, F1-score, and ROC-AUC. Among the models, XGBoost outperformed others, achieving the highest F1-score and ROC-AUC, followed by Random Forest. Feature importance analysis and SHAP value interpretation provided insights into key predictors contributing to software faults, enhancing the model's transparency and practical usability. The study demonstrates that ensemble learning methods significantly improve the fault prediction process, making them suitable for deployment in real-world software quality assurance workflows. These findings contribute to the ongoing advancement of predictive software engineering and open new avenues for integrating intelligent fault detection systems in continuous integration and DevOps environments.