QUARE: Multi-Agent Negotiation for Balancing Quality Attributes in Requirements Engineering
QUARE is a multi-agent framework for requirements engineering that explicitly turns conflicts among different quality attributes into a "negotiation" process, rather than having a single LLM make implicit trade-offs.…
Summary
QUARE is a multi-agent framework for requirements engineering that explicitly turns conflicts among different quality attributes into a "negotiation" process, rather than having a single LLM make implicit trade-offs. Its core claim is that, compared with larger models, structured role division, negotiation protocols, and automated verification are more effective at improving the quality of requirements analysis.
Problem
- In requirements engineering, it is often necessary to satisfy mutually conflicting quality attributes such as safety, efficiency, sustainability, trustworthiness, and responsibility/compliance at the same time, and manually balancing these constraints is both time-consuming and error-prone.
- Existing LLM methods are mostly based on monolithic reasoning or implicit aggregation, making it difficult to explicitly surface conflicts, explain the rationale behind trade-offs, and preserve stakeholder intent.
- Requirement issues are critical in software projects; the paper notes that more than 70% of failed projects can be traced to requirements-related defects, so automated and traceable requirements analysis is important.
Approach
- QUARE decomposes requirements analysis into 5 quality-specialized agents (Safety, Efficiency, Green, Trustworthiness, Responsibility) plus 1 orchestrator. All agents share the same LLM backbone, but role isolation is achieved through different system prompts.
- It uses a dialectical negotiation protocol: agents first propose requirements, then other agents critique constraint conflicts, and finally the coordinator synthesizes the outcome; conflicts are categorized into resource-bound and logical incompatibility, and negotiation proceeds for at most 3 rounds.
- To identify conflicts, the system first uses a BERT embedding cosine similarity threshold of 0.85 to find potential overlaps, and then uses an LLM to determine whether they are redundant or one of the two conflict types.
- The negotiated results are converted into KAOS goal models, followed by topology/DAG validation, rule checking, and RAG-supported hallucination and compliance verification (such as ISO 26262, ISO 27001), and finally output as standardized engineering artifacts.
- In the experiments, gpt-4o-mini-2024-07-18 was used on 5 case studies (the MARE and iReDev benchmarks and an industrial autonomous-driving specification), compared against single-agent, multi-agent without negotiation, MARE, and iReDev.
Results
- The paper claims that QUARE achieves 98.2% compliance coverage, representing a +105% improvement over the baseline.
- It reaches 94.9% semantic preservation, which is +2.3 percentage points higher than the best baseline.
- The verifiability score reaches 4.96/5.0.
- The number of generated requirements is 25–43% higher than in existing multi-agent RE frameworks.
- Negotiation converged within the 3-round limit in all scenarios; the experiments used 3 random seeds, a unified configuration, and 180 total runs.
- The abstract and excerpt do not provide a complete itemized table of values for each dataset and each baseline, but the strongest quantitative conclusion is that QUARE overall outperforms the compared methods in compliance coverage, semantic preservation, verifiability, and requirement output volume.
Link
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