这篇论文用49个程序员和148个实例，实测了GitHub Copilot评估HIPAA合规NFR的准确度，发现开发者容易被带偏，但主动交互反而让人更满意。
本研究聚焦LLM对话系统在处理非功能需求（NFRs）时的准确性和对话质量。49名程序员使用GitHub Copilot对148个HIPAA衍生NFR进行评估，基于iTrust代码库，从需求满足度、推理和代码定位三个维度分析。结果显示开发者倾向于认同LLM评估，但与专家标注的真实标准相比准确率较低。更长系统响应和更多信息提供轮次会降低用户满意度，而主动交互则提升满意度。论文为设计面向NFR评估的LLM对话系统提供了经验证据。
Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment
LLM-based dialogue assistants have become mainstream tools for software developers, yet current evaluation benchmarks focus exclusively on functional correctness. This leaves a critical gap in assessing the quality and accuracy of these conversations when handling Non-Functional Requirements (NFRs), which are inherently vague, context-dependent, and involve many parts of a program. Evaluating how well these systems support collaborative reasoning about NFRs requires methods that go beyond single-turn accuracy to capture both the correctness of the system's outputs and the quality of the multi-turn interaction. In this paper, we investigate the accuracy and quality of multi-turn conversations between developers and an LLM-based agent in the domain of Health Insurance Portability and Accountability Act (HIPAA) regulatory compliance. We hired 49 programmers to interact with GitHub Copilot to assess 148 HIPAA-derived NFRs against the iTrust codebase, a system designed to comply with HIPAA regulations, across three dimensions: requirement satisfaction level, reasoning, and code localization. We find that developers tend to agree with LLM assessments, but accuracy against expert ground truth is low. We model user satisfaction and find that longer system responses and more information-providing turns negatively affect user satisfaction, whereas proactive interactions positively affect it. Our findings provide insights for designing LLM-based dialogue systems that support NFR assessment.