mlsanityData to 14 Oct 2026

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

arXiv 2310.13976 ↗First posted 21 Oct 2023cs.SEcited by 42

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 11; 2025: 19; 2026: 10.

42 papers.

Sort:

By area:

  1. 2024Large Language Model-Based Agents for Software Engineering: A Surveycited by 178
  2. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 86
  3. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  4. 2024MARE: Multi-Agents Collaboration Framework for Requirements Engineeringcited by 28
  5. 2024The Future of AI-Driven Software Engineeringcited by 23
  6. 2024Using LLMs in Software Requirements Specifications: An Empirical Evaluationcited by 20
  7. 2024Model Generation with LLMs: From Requirements to UML Sequence Diagramscited by 11
  8. 2024Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Studycited by 9
  9. 2025Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Modelscited by 8
  10. 2023Model-driven Engineering for Machine Learning Components: A Systematic Literature Reviewcited by 7
  11. 2025CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generationcited by 6
  12. 2024CoDefeater: Using LLMs To Find Defeaters in Assurance Casescited by 5
30 more
  1. 2026Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarioscited by 5
  2. 2025Unveiling AI's Threats to Child Protection: Regulatory efforts to Criminalize AI-Generated CSAM and Emerging Children's Rights Violationscited by 4
  3. 2024GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Trainingcited by 3
  4. 2025Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scalecited by 3
  5. 2025Can LLMs Generate User Stories and Assess Their Quality?cited by 2
  6. 2026GenAI-DrawIO-Creator: A Framework for Automated Diagram Generationcited by 2
  7. 2026PaperRepro: Automated Computational Reproducibility Assessment for Social Science Paperscited by 2
  8. 2024Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approachcited by 1
  9. 2024The importance of visual modelling languages in generative software engineeringcited by 1
  10. 2025Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Studycited by 1
  11. 2025A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standardscited by 1
  12. 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systemscited by 1
  13. 2025TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Frameworkcited by 1
  14. 2025U2F: Encouraging SWE-Agent to Seize Novelty without Losing Feasibilitycited by 1
  15. 2026Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agentscited by 1
  16. 2026An Interactive Agent for Requirement-Driven Candidate Sourcingcited by 1
  17. 2025Systems Engineering for Autonomous Vehicles; Supervising AI using Large Language Models (SSuperLLM)cited by 0
  18. 2025ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generationcited by 0
  19. 2025LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management Systemcited by 0
  20. 2025Generating Privacy Stories From Software Documentationcited by 0
  21. 2025Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathycited by 0
  22. 2025Automatic Classification of User Requirements from Online Feedback -- A Replication Studycited by 0
  23. 2025Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Developmentcited by 0
  24. 2025Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Studycited by 0
  25. 2025AI for Requirements Engineering: Industry adoption and Practitioner perspectivescited by 0
  26. 2026QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiationcited by 0
  27. 2026From Reviews to Requirements: Can LLMs Generate Human-Like User Stories?cited by 0
  28. 2026ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiationcited by 0
  29. 2026Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessmentcited by 0
  30. 2026From Regulation to Requirements: An Automated Requirement Derivation and Explanation Pipelinecited by 0