Automating requirements quality analysis remains challenging because multiple, often conflicting quality attributes must be balanced while preserving stakeholder intent. Existing Large-Language-Model (LLM) approaches predominantly rely on task-oriented decomposition or implicit aggregation, limiting their ability to systematically surface and resolve cross-quality conflicts. We present QUARE (QUality-Aware REquirements Analysis), a multi-agent framework that takes a project description as input and formulates requirements quality analysis as structured negotiation among five quality-specialized agents: Safety, Efficiency, Green, Trustworthiness, and Responsibility, coordinated by a dedicated orchestrator. QUARE introduces a dialectical negotiation protocol that explicitly exposes inter-quality conflicts and resolves them through iterative proposal, critique, and synthesis. Negotiated outcomes are transformed into structurally sound KAOS goal models via topology validation and verified against industry standards through retrieval-augmented generation (RAG). We evaluate QUARE on five benchmark systems drawn from established RE benchmarks, MARE and iReDev, and an industrial autonomous-driving specification, spanning safety-critical, financial, and information-system domains. Results show that QUARE achieves 98.2% compliance coverage, a 105% improvement over both baselines; 94.9% semantic preservation, a 2.3 percentage-point improvement over the best baseline; and high verifiability, with a score of 4.96 out of 5.0, while generating 25-43% more requirements than existing multi-agent RE frameworks. These findings suggest that, when using capable instruction-tuned models, architectural choices such as quality-dimension decomposition, explicit negotiation, and automated verification may contribute more to output quality than model scale alone.

翻译：摘要：自动化需求质量分析仍面临挑战，因为必须在维护利益相关者意图的同时，平衡多个往往相互冲突的质量属性。现有的大语言模型方法主要依赖于面向任务的分解或隐式聚合，这限制了其系统性地揭示和解决跨质量冲突的能力。本文提出QUARE（质量感知需求分析框架），这是一个多智能体框架，以项目描述作为输入，将需求质量分析构建为由五个质量专用智能体（安全性、效率、绿色性、可信赖性和责任性）在专用协调器统一调度下进行的结构化协商过程。QUARE引入了一种辩证协商协议，该协议能够显式暴露跨质量冲突，并通过迭代式的提议、批评和综合过程加以解决。协商结果经过拓扑验证转化为结构严谨的KAOS目标模型，并通过检索增强生成技术依据行业标准进行验证。我们在来自成熟RE基准测试MARE和iReDev的五个基准系统，以及一份工业级自动驾驶规范上对QUARE进行了评估，这些系统涵盖了安全关键、金融和信息系统领域。结果表明，QUARE实现了98.2%的合规覆盖率，相比两个基线提升了105%；语义保持率达到94.9%，相比最佳基线提升了2.3个百分点；可验证性较高，得分为4.96分（满分5.0分）；同时生成的需求数量比现有的多智能体RE框架多出25%-43%。这些发现表明，在使用能力型指令微调模型时，质量维度分解、显式协商和自动验证等架构选择可能比模型规模本身对输出质量的贡献更大。