The adoption of chatbots into software development tasks has become increasingly popular among practitioners, driven by the advantages of cost reduction and acceleration of the software development process. Chatbots understand users’ queries through the Natural Language Understanding component (NLU). To yield reasonable performance, NLUs have to be trained with extensive, high-quality datasets, that express a multitude of ways users may interact with chatbots. However, previous studies show that creating a high-quality training dataset for software engineering chatbots is expensive in terms of both resources and time.

Therefore, in this paper, we present an automated transformer-based approach to augment software engineering chatbot datasets. Our approach combines traditional natural language processing techniques with the BART transformer to augment a dataset by generating queries through synonym replacement and paraphrasing. We evaluate the impact of using the augmentation approach on the NLU’s performance using three software engineering datasets. Overall, the augmentation approach shows promising results in improving the NLU’s performance, augmenting queries with varying sentence structures while preserving their original semantics. Furthermore, it increases the NLU’s confidence in its intent classification for the correctly classified intents. We believe that our study helps practitioners to improve the performance of their chatbots and guides future research to propose augmentation techniques for SE chatbots.