This document discusses using machine learning techniques to predict software bugs based on historical data. Specifically, it compares the performance of the Naive Bayes and J48 (Decision Tree) classifiers on bug prediction. The Naive Bayes and J48 classifiers are trained on datasets from real software projects containing product metrics and defect information. Their performance is evaluated based on accuracy, F-measure, recall, and precision. The results show that the J48 Decision Tree classifier has the best performance and is more accurate at predicting bugs compared to the Naive Bayes classifier. The authors conclude that machine learning is an effective approach for software bug prediction and can improve software quality if used early in the development process.