Software (SW) systems experience faults after deployment, raising concerns about reliability and leading to financial losses, reputational damage, and safety risks. This paper presents a novel approach using CodeBERT, a state-of-the-art neural code representation model pre-trained in multi-programming languages and employs various code metrics to predict SW faults. The study comprehensively evaluates trained models by analyzing publicly available codebase and employing diverse machine learning models, feature selection techniques, and class balancing through SMOTE. The results show that SMOTE significantly enhances vulnerability detection performance, particularly in accuracy, AUC, sensitivity, and specificity. The EXTR classifier consistently outperforms others, with an average AUC of 0.82, and the features selected using the GA feature selection technique, despite achieving a mean AUC of 0.84. Interestingly, among employed embedding techniques, SW metrics combined with CodeBERT (SMCBERT) stand out as top performers, achieving the highest mean AUC score of 0.80, making models trained on SMCBERT the best for SW vulnerability prediction.