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Artificial Intelligence and Machine Learning have witnessed rapid,significant improvements in Natural Language Processing (NLP) tasks. UtilizingDeep Learning, researchers have taken advantage of repository comments inSoftware Engineering to produce accurate methods for detecting Self-AdmittedTechnical Debt (SATD) from 20 open-source Java projects' code. In this work, weimprove SATD detection with a novel approach that leverages the BidirectionalEncoder Representations from Transformers (BERT) architecture. For comparison,we re-evaluated previous deep learning methods and applied stratified 10-foldcross-validation to report reliable F$_1$-scores. We examine our model in bothcross-project and intra-project contexts. For each context, we use re-samplingand duplication as augmentation strategies to account for data imbalance. Wefind that our trained BERT model improves over the best performance of allprevious methods in 19 of the 20 projects in cross-project scenarios. However,the data augmentation techniques were not sufficient to overcome the lack ofdata present in the intra-project scenarios, and existing methods still performbetter. Future research will look into ways to diversify SATD datasets in orderto maximize the latent power in large BERT models.

Measuring Improvement of F$_1$-Scores in Detection of Self-Admitted Technical Debt