Normative Requirements Operationalization with Large Language Models

TL;DR

This paper introduces a novel approach using Large Language Models to extract semantic relationships for operationalizing and analyzing normative non-functional requirements, enhancing consistency checks with real-world case studies.

Contribution

It presents a complementary method leveraging LLMs to improve the elicitation and analysis of normative requirements beyond formal rule-based techniques.

Findings

Effective extraction of semantic relationships demonstrated

Improved consistency analysis of normative requirements

Successful application in real-world case studies

Abstract

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.