  Natural Language Processing (NLP) for Requirements Engineering (RE) (NLP4RE)
seeks to apply NLP tools, techniques, and resources to the RE process to
increase the quality of the requirements. There is little research involving
the utilization of Generative AI-based NLP tools and techniques for
requirements elicitation. In recent times, Large Language Models (LLM) like
ChatGPT have gained significant recognition due to their notably improved
performance in NLP tasks. To explore the potential of ChatGPT to assist in
requirements elicitation processes, we formulated six questions to elicit
requirements using ChatGPT. Using the same six questions, we conducted
interview-based surveys with five RE experts from academia and industry and
collected 30 responses containing requirements. The quality of these 36
responses (human-formulated + ChatGPT-generated) was evaluated over seven
different requirements quality attributes by another five RE experts through a
second round of interview-based surveys. In comparing the quality of
requirements generated by ChatGPT with those formulated by human experts, we
found that ChatGPT-generated requirements are highly Abstract, Atomic,
Consistent, Correct, and Understandable. Based on these results, we present the
most pressing issues related to LLMs and what future research should focus on
to leverage the emergent behaviour of LLMs more effectively in natural
language-based RE activities.
