# AI based Multiagent Approach for Requirements Elicitation and Analysis

arXiv: https://arxiv.org/abs/2409.00038 · HTML: https://mlsanity.com/paper/2409.00038/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2024.

## Built on by (most cited first, 6 of 6)

- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [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)
- [U2F: Encouraging SWE-Agent to Seize Novelty without Losing Feasibility](https://mlsanity.com/paper/2511.03517/) (2025)
- [ReqFusion: A Multi-Provider Framework for Automated PEGS Analysis Across Software Domains](https://mlsanity.com/paper/2603.23482/) (2026)
- [Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations](https://mlsanity.com/paper/2604.22207/) (2026)
- [From Obligation to Specification: A Survey on Validating EU AI Act Requirements in RE](https://mlsanity.com/paper/2607.21608/) (2026)

## Abstract (arXiv)

Requirements Engineering (RE) plays a pivotal role in software development, encompassing tasks such as requirements elicitation, analysis, specification, and change management. Despite its critical importance, RE faces challenges including communication complexities, early-stage uncertainties, and accurate resource estimation. This study empirically investigates the effectiveness of utilizing Large Language Models (LLMs) to automate requirements analysis tasks. We implemented a multi-agent system that deploys AI models as agents to generate user stories from initial requirements, assess and improve their quality, and prioritize them using a selected technique. In our implementation, we deployed four models, namely GPT-3.5, GPT-4 Omni, LLaMA3-70, and Mixtral-8B, and conducted experiments to analyze requirements on four real-world projects. We evaluated the results by analyzing the semantic similarity and API performance of different models, as well as their effectiveness and efficiency in requirements analysis, gathering users' feedback on their experiences. Preliminary results indicate notable variations in task completion among the models. Mixtral-8B provided the quickest responses, while GPT-3.5 performed exceptionally well when processing complex user stories with a higher similarity score, demonstrating its capability in deriving accurate user stories from project descriptions. Feedback and suggestions from the four project members further corroborate the effectiveness of LLMs in improving and streamlining RE phases.