Machine Learning for Software Defect Prediction

This review paper explores the application of machine learning techniques in software defect prediction to improve software quality and reliability. It discusses algorithms such as logistic regression, decision trees, random forests, support vector machines, gradient boosting, and neural networks. The paper examines software metrics, code change history, dataset preparation, evaluation methods, and model validation. It also highlights challenges such as class imbalance, data leakage, false predictions, and model interpretability. Finally, it presents practical recommendations for using defect prediction models to prioritize software testing, code reviews, and maintenance activities.