A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets

TL;DR

This paper introduces a transformer-based method to automatically augment software engineering chatbot datasets, enhancing natural language understanding performance by generating diverse, semantically consistent queries.

Contribution

It presents a novel combination of NLP techniques with BART transformer for dataset augmentation in SE chatbots, improving intent classification accuracy.

Findings

Augmentation improves Rasa NLU performance.

Generated queries vary in structure but retain semantics.

Confidence in intent classification increases with augmentation.

Abstract

Background: 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. Aims: Therefore, in this paper, we present an automated transformer-based approach to augment software engineering chatbot datasets. Method: 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 Rasa NLU's performance using three software engineering datasets. Results: Overall, the augmentation approach shows promising results in improving the Rasa's performance, augmenting queries with varying sentence structures while preserving their original semantics. Furthermore, it increases Rasa's confidence in its intent classification for the correctly classified intents. Conclusions: We believe that our study helps practitioners improve the performance of their chatbots and guides future research to propose augmentation techniques for SE chatbots.