# MARE: Multi-Agents Collaboration Framework for Requirements Engineering

arXiv: https://arxiv.org/abs/2405.03256 · HTML: https://mlsanity.com/paper/2405.03256/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 6 May 2024.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability](https://mlsanity.com/paper/2308.00229/) (2023)
- [How Effective are Large Language Models in Generating Software Specifications?](https://mlsanity.com/paper/2306.03324/) (2023)
- [Zero-Shot Learning for Requirements Classification: An Exploratory Study](https://mlsanity.com/paper/2302.04723/) (2023)
- [Where is Your App Frustrating Users?](https://mlsanity.com/paper/2204.09310/) (2022)
- [Deep Learning Models in Software Requirements Engineering](https://mlsanity.com/paper/2105.07771/) (2021)
- [FREPA: An Automated and Formal Approach to Requirement Modeling and Analysis in Aircraft Control Domain](https://mlsanity.com/paper/2306.01260/) (2023)
- [Towards dialogue based, computer aided software requirements elicitation](https://mlsanity.com/paper/2310.13953/) (2023)

## Built on by (most cited first, 28 of 28)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents](https://mlsanity.com/paper/2407.01511/) (2024)
- [CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging](https://mlsanity.com/paper/2502.05664/) (2025)
- [Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks](https://mlsanity.com/paper/2508.13143/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2601.09822/) (2026)
- [CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation](https://mlsanity.com/paper/2504.13472/) (2025)
- [OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices](https://mlsanity.com/paper/2510.24145/) (2025)
- [Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2605.14892/) (2026)
- [Using LLMs in Generating Design Rationale for Software Architecture Decisions](https://mlsanity.com/paper/2504.20781/) (2025)
- [Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach](https://mlsanity.com/paper/2510.12120/) (2025)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [REAgent: Requirement-Driven LLM Agents for Software Issue Resolution](https://mlsanity.com/paper/2604.06861/) (2026)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [Multi-Agent LLMs as Ethics Advocates for AI-Based Systems](https://mlsanity.com/paper/2507.08392/) (2025)
- [MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration](https://mlsanity.com/paper/2507.21382/) (2025)
- [TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework](https://mlsanity.com/paper/2510.17163/) (2025)
- [From LLMs to Agents: A Comparative Evaluation of LLMs and LLM-based Agents in Security Patch Detection](https://mlsanity.com/paper/2511.08060/) (2025)
- [Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models](https://mlsanity.com/paper/2607.16708/) (2026)
- [An Interactive Agent for Requirement-Driven Candidate Sourcing](https://mlsanity.com/paper/2608.23501/) (2026)
- [Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking](https://mlsanity.com/paper/2610.10628/) (2026)
- [Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision](https://mlsanity.com/paper/2506.22656/) (2025)
- [4D-ARE: Bridging the Attribution Gap in LLM Agent Requirements Engineering](https://mlsanity.com/paper/2601.04556/) (2026)
- [QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation](https://mlsanity.com/paper/2603.11890/) (2026)
- [ReqFusion: A Multi-Provider Framework for Automated PEGS Analysis Across Software Domains](https://mlsanity.com/paper/2603.23482/) (2026)
- [ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation](https://mlsanity.com/paper/2604.23124/) (2026)
- [Cluster-Aware Dual-Level Test Specification Generation for Large-Scale Automotive Software Requirements](https://mlsanity.com/paper/2606.17197/) (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) is a critical phase in the software development process that generates requirements specifications from stakeholders' needs. Recently, deep learning techniques have been successful in several RE tasks. However, obtaining high-quality requirements specifications requires collaboration across multiple tasks and roles. In this paper, we propose an innovative framework called MARE, which leverages collaboration among large language models (LLMs) throughout the entire RE process. MARE divides the RE process into four tasks: elicitation, modeling, verification, and specification. Each task is conducted by engaging one or two specific agents and each agent can conduct several actions. MARE has five agents and nine actions. To facilitate collaboration between agents, MARE has designed a workspace for agents to upload their generated intermediate requirements artifacts and obtain the information they need. We conduct experiments on five public cases, one dataset, and four new cases created by this work. We compared MARE with three baselines using three widely used metrics for the generated requirements models. Experimental results show that MARE can generate more correct requirements models and outperform the state-of-the-art approaches by 15.4%. For the generated requirements specifications, we conduct a human evaluation in three aspects and provide insights about the quality