# Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs

arXiv: https://arxiv.org/abs/2310.13976 · HTML: https://mlsanity.com/paper/2310.13976/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2023.

## Built on by (most cited first, 30 of 42)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [MARE: Multi-Agents Collaboration Framework for Requirements Engineering](https://mlsanity.com/paper/2405.03256/) (2024)
- [The Future of AI-Driven Software Engineering](https://mlsanity.com/paper/2406.07737/) (2024)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [Model Generation with LLMs: From Requirements to UML Sequence Diagrams](https://mlsanity.com/paper/2404.06371/) (2024)
- [Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study](https://mlsanity.com/paper/2404.12772/) (2024)
- [Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models](https://mlsanity.com/paper/2507.05981/) (2025)
- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation](https://mlsanity.com/paper/2504.13472/) (2025)
- [CoDefeater: Using LLMs To Find Defeaters in Assurance Cases](https://mlsanity.com/paper/2407.13717/) (2024)
- [Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios](https://mlsanity.com/paper/2604.06742/) (2026)
- [Unveiling AI's Threats to Child Protection: Regulatory efforts to Criminalize AI-Generated CSAM and Emerging Children's Rights Violations](https://mlsanity.com/paper/2503.00433/) (2025)
- [GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training](https://mlsanity.com/paper/2406.11439/) (2024)
- [Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale](https://mlsanity.com/paper/2504.09283/) (2025)
- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [GenAI-DrawIO-Creator: A Framework for Automated Diagram Generation](https://mlsanity.com/paper/2601.05162/) (2026)
- [PaperRepro: Automated Computational Reproducibility Assessment for Social Science Papers](https://mlsanity.com/paper/2603.00058/) (2026)
- [Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach](https://mlsanity.com/paper/2408.17404/) (2024)
- [The importance of visual modelling languages in generative software engineering](https://mlsanity.com/paper/2411.17976/) (2024)
- [Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study](https://mlsanity.com/paper/2502.00015/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Multi-Agent LLMs as Ethics Advocates for AI-Based Systems](https://mlsanity.com/paper/2507.08392/) (2025)
- [TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework](https://mlsanity.com/paper/2510.17163/) (2025)
- [U2F: Encouraging SWE-Agent to Seize Novelty without Losing Feasibility](https://mlsanity.com/paper/2511.03517/) (2025)
- [Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents](https://mlsanity.com/paper/2605.18684/) (2026)
- [An Interactive Agent for Requirement-Driven Candidate Sourcing](https://mlsanity.com/paper/2608.23501/) (2026)
- [Systems Engineering for Autonomous Vehicles; Supervising AI using Large Language Models (SSuperLLM)](https://mlsanity.com/paper/2501.10839/) (2025)
- [ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation](https://mlsanity.com/paper/2505.17632/) (2025)

## Abstract (arXiv)

Requirements Engineering (RE) is a critical phase in software development including the elicitation, analysis, specification, and validation of software requirements. Despite the importance of RE, it remains a challenging process due to the complexities of communication, uncertainty in the early stages and inadequate automation support. In recent years, large-language models (LLMs) have shown significant promise in diverse domains, including natural language processing, code generation, and program understanding. This chapter explores the potential of LLMs in driving RE processes, aiming to improve the efficiency and accuracy of requirements-related tasks. We propose key directions and SWOT analysis for research and development in using LLMs for RE, focusing on the potential for requirements elicitation, analysis, specification, and validation. We further present the results from a preliminary evaluation, in this context.