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。由于这些需求通常由不同专业背景和优先级（伦理学家、律师、社会科学家等）的非技术系统利益相关者定义，确保其规范完善性和一致性极具挑战性。近期研究通过使用领域特定语言将规范性需求定义为规则，并利用形式化方法分析其一致性来应对这一挑战。本文提出了一种互补性方法，利用大语言模型提取系统能力抽象表征之间的语义关系。这些关系通常由非技术利益相关者（例如基于常识或领域知识）隐含假设，进而被用于丰富自动化推理技术，以启发和验证规范性需求的一致性。我们通过一系列真实案例研究，展示了本方法在规范性需求启发与操作化方面的有效性。