Normative non-functional requirements specify constraints that a system must observe in order to avoid violations of social, legal, ethical, empathetic, and cultural norms. As these requirements are typically defined by non-technical system stakeholders with different expertise and priorities (ethicists, lawyers, social scientists, etc.), ensuring their well-formedness and consistency is very challenging. Recent research has tackled this challenge using a domain-specific language to specify normative requirements as rules whose consistency can then be analysed with formal methods. In this paper, we propose a complementary approach that uses Large Language Models to extract semantic relationships between abstract representations of system capabilities. These relations, which are often assumed implicitly by non-technical stakeholders (e.g., based on common sense or domain knowledge), are then used to enrich the automated reasoning techniques for eliciting and analyzing the consistency of normative requirements. We show the effectiveness of our approach to normative requirements elicitation and operationalization through a range of real-world case studies.

翻译：规范性非功能性需求规定了系统必须遵守的约束，以避免违反社会、法律、伦理、共情及文化规范。由于此类需求通常由具有不同专业背景与优先级的非技术性系统利益相关方（如伦理学家、律师、社会科学家等）定义，确保其形式规范性与一致性极具挑战。近期研究通过采用领域特定语言将规范性需求表述为规则，并利用形式化方法分析其一致性，以应对这一挑战。本文提出一种互补性方法，利用大型语言模型从系统能力的抽象表征中提取语义关系。这些常被非技术利益相关方（基于常识或领域知识）隐式假定的关系，可用于增强自动化推理技术，以引导并分析规范性需求的一致性。我们通过一系列真实案例研究，展示了该方法在规范性需求引导与可操作化方面的有效性。