
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.


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improving-the-identification-and-categorization-of-self-admitted-technical-debt-using-deep-learning-and-data-augmentation


Improving the Identification and Categorization of Self-Admitted Technical Debt Using Deep Learning and Data Augmentation
