# Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes

arXiv: https://arxiv.org/abs/2307.07381 · HTML: https://mlsanity.com/paper/2307.07381/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2023.

## Built on by (most cited first, 25 of 25)

- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [LLM-based agents for automating the enhancement of user story quality: An early report](https://mlsanity.com/paper/2403.09442/) (2024)
- [The Impact of Prompt Programming on Function-Level Code Generation](https://mlsanity.com/paper/2412.20545/) (2024)
- [Model Generation with LLMs: From Requirements to UML Sequence Diagrams](https://mlsanity.com/paper/2404.06371/) (2024)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [Accelerated Preference Elicitation with LLM-Based Proxies](https://mlsanity.com/paper/2501.14625/) (2025)
- [Had enough of experts? Quantitative knowledge retrieval from large language models](https://mlsanity.com/paper/2402.07770/) (2024)
- [GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training](https://mlsanity.com/paper/2406.11439/) (2024)
- [Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction](https://mlsanity.com/paper/2408.01063/) (2024)
- [On the Utility of Domain Modeling Assistance with Large Language Models](https://mlsanity.com/paper/2410.12577/) (2024)
- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [ReqInOne: A Large Language Model-Based Agent for Software Requirements Specification Generation](https://mlsanity.com/paper/2508.09648/) (2025)
- [Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach](https://mlsanity.com/paper/2408.17404/) (2024)
- [A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development](https://mlsanity.com/paper/2505.07664/) (2025)
- [Many Hands Make Light Work: An LLM-based Multi-Agent System for Detecting Malicious PyPI Packages](https://mlsanity.com/paper/2601.12148/) (2026)
- [ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation](https://mlsanity.com/paper/2505.17632/) (2025)
- [LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management System](https://mlsanity.com/paper/2505.18019/) (2025)
- [Generating Privacy Stories From Software Documentation](https://mlsanity.com/paper/2506.23014/) (2025)
- [Automatic Classification of User Requirements from Online Feedback -- A Replication Study](https://mlsanity.com/paper/2507.21532/) (2025)
- [AI for Requirements Engineering: Industry adoption and Practitioner perspectives](https://mlsanity.com/paper/2511.01324/) (2025)
- [The Software Engineering Simulations Lab: Agentic AI for RE Quality Simulations](https://mlsanity.com/paper/2511.17762/) (2025)
- [QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation](https://mlsanity.com/paper/2603.11890/) (2026)
- [ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation](https://mlsanity.com/paper/2604.23124/) (2026)
- [ReqGenX: An Empirical Study of Atomic Decomposition, Artifact Regeneration, and Reconstruction for Legacy SRS Documents](https://mlsanity.com/paper/2607.16564/) (2026)
- [AI-assisted Script Management for Requirements Elicitation Interviews](https://mlsanity.com/paper/2608.01640/) (2026)

## Abstract (arXiv)

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.