From Law to Gherkin: A Human-Centred Quasi-Experiment on the Quality of LLM-Generated Behavioural Specifications from Food-Safety Regulations

Context: Laws and regulations increasingly affect software design and quality assurance, but legal texts are written in technology-neutral language. This creates challenges for engineers who must develop compliance artifacts such as requirements and acceptance criteria. Manual creation is labor-intensive, error-prone, and requires domain expertise. Advances in Generative AI (GenAI), especially Large Language Models (LLMs), offer a way to automate deriving such artifacts. Objective: We present the first systematic human-subject study of LLMs' ability to derive behavioral specifications from legal texts using a quasi-experimental design. These specifications translate legal requirements into a developer-friendly form. Methods: Ten participants evaluated specifications generated from food-safety regulations by Claude and Llama. Using Gherkin, a structured BDD language, 60 specifications were produced. Each participant assessed 12 across five criteria: Relevance, Clarity, Completeness, Singularity, and Time Savings. Each specification was reviewed by two participants, yielding 120 assessments. Results: For Relevance, 75% of ratings were highest and 20% second-highest. Clarity reached 90% highest. Completeness: 75% highest, 19% second. Singularity: 82% highest, 12% second. Time Savings: 68% highest, 24% second. No lowest ratings occurred. Mann-Whitney U tests showed no significant differences across participants or models. Llama slightly outperformed Claude in Clarity, Completeness, and Time Savings, while Claude was stronger in Singularity. Feedback noted hallucinations and omissions but confirmed the utility of the specifications. Conclusion: LLMs can generate high-quality Gherkin specifications from legal texts, reducing manual effort and providing structured artifacts useful for implementation, assurance, and test generation.