Abstract

Software reliability is a critical concern in modern software engineering, as defects in software systems can lead to financial loss, security vulnerabilities, and system failures. Automated defect prediction (ADP) models aim to identify fault-prone components in software systems during the development lifecycle, enabling targeted testing and maintenance. This paper explores various computational approaches for automated defect prediction, including machine learning, deep learning, ensemble methods, and statistical models. Techniques such as support vector machines (SVM), random forests, neural networks, and hybrid models are examined for their effectiveness in predicting defects using software metrics, historical bug data, and code characteristics. The study emphasizes challenges such as data imbalance, noisy and incomplete datasets, cross-project prediction, and feature selection. Additionally, the integration of advanced feature engineering, software process metrics, and developer activity metrics is discussed to improve predictive accuracy. Evaluation metrics, including precision, recall, F1-score, ROC-AUC, and cost-effectiveness, are analyzed for model validation. The advantages and limitations of each approach are summarized to guide software engineers and researchers in selecting optimal ADP models. The findings highlight the potential of automated defect prediction to enhance software reliability, reduce maintenance costs, and improve overall software quality