Automated Classification of Code Review Comments using Deep Neural Network-based Architecture - Gobind Panditrao (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Code review comments are essential components for automated code review systems that facilitate software quality and productivity of developers. This study demonstrates the classification of code review comments using Deep Neural Networks with a hybrid architecture consisting of CodeBERT and Long Short-Term Memory. Leveraging a dataset from the OpenDev Nova initiative, this study employed a five-class classification model to identify specific types of review comments, like discussions, document changes, and false positives. The approach was modifying and retraining the model already proposed in existing literature and then adapting it to the project environment by restoring the required attributes using the standard libraries. The performance of this modified model was observed across different epochs, with precision, recall, F1-score, and accuracy metrics being utilized to establish its efficiency. The main results indicated major enhancements to the handling of complex comment types as well and overall accuracy compared to previously established models. After analysis, this research supports the viability of Deep Neural Networks in providing a reliable classification system that considers code nuances and contexts. The research also identifies the limitations of the generalizability of the study results due to dataset specificity and suggests possible ways of overcoming this problem, including the use of different neural network architectures and the inclusion of more development environment types in the datasets.