Improving the Identification and Categorization of Self-Admitted Technical Debt Using Deep Learning and Data Augmentation
Deep learning models, enhanced by data augmentation techniques, can significantly improve the identification and categorization of self-admitted technical debt (SATD) in software artifacts like code comments, issue trackers, pull requests, and commit messages.