arXiv：cs.AI· Yongjian Tang, Linhan Li, Thomas Runkler·· 3 小时前
Agent4RE：面向端到端软件需求工程的自改进多智能体框架与基准
Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking
AI 导读
研究人员发布 Agent4RE，一个面向端到端软件需求工程（RE）的自改进多智能体系统，并构建了覆盖需求获取到生成的 RE-E2E 基准。在 8 个 LLM 上的评测显示，Agent4RE 三个版本在文本指标上平均超越领域上下文增强提示基线 8%。加入自主自改进或结构化人工反馈的两个增强版本在 LLM-as-a-judge 和人工评分中最高，以约 0.8 分（四分制）超过两个 RE 基线。
正文
Abstract:Existing LLM-based approaches for software Requirements Engineering (RE) typically rely on basic prompting strategies or rudimentary agent collaboration, under-utilizing the full potential of multi-agent systems. Meanwhile, available datasets focus on isolated subtasks, such as requirements extraction, classification, and completeness detection, leaving the absence of an end-to-end RE benchmark that spans from requirements elicitation to generation. We present Agent4RE - a self-refining multi-agent RE system that orchestrates specialized agents and incorporates two iterative improvement loops. To support evaluation, we construct RE-E2E - a real-world dataset built from human-written requirement specifications, enabling end-to-end assessment of RE workflows. Building on this foundation, we further propose two enhanced Agent4RE versions that incorporate either autonomous self-refinement or structured human feedback, and analyze their strengths and limitations across different scenarios. Evaluation on 8 Large Language Models (LLMs) demonstrates that all three Agent4RE variants consistently outperform a domain-context-augmented prompting baseline by average 8% in text-based metrics. The two enhanced variants achieve the highest LLM-as-a-judge and human ratings, surpassing two RE baselines by approximately 0.8 points on a four-point scale. This consistent performance establishes Agent4RE as a practical end-to-end RE solution for industrial environments.
| Comments: | Accepted to the ASE@POVC track; The E2E requirements engineering benchmark is available this https URL |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.10628 [cs.SE] |
| (or arXiv:2610.10628v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10628 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1145/3843779.3844634
DOI(s) linking to related resources |
Submission history
From: Yongjian Tang [view email]
[v1]
Wed, 7 Oct 2026 11:23:11 UTC (4,027 KB)
来源：arXiv：cs.AI · arxiv.org