LLM ‐Based Domain Model Extraction From Requirements: A Case Study on Smart Tourism Applications

Automating the extraction of domain models from natural language requirements remains a central challenge in model‐driven engineering (MDE), particularly at the level of the computation‐independent model (CIM), the most abstract layer of the model‐driven architecture (MDA). This paper introduces an approach that uses large language models (LLMs) to generate domain models from software requirements. Unlike prior work that relies on a single monolithic prompt or on generic task decomposition, our approach organises the modelling task as a modular chain of prompts with dedicated stages for enumerations, entities, and relationships, each producing an explicit intermediate output that feeds the next stage before a final consolidation step. The approach is implemented as a Visual Studio Code extension called SmartModel, which integrates generation, visualisation, and requirement‐level traceability into the developer's environment. To evaluate the approach, a case study on smart tourism software requirements was conducted, comparing the LLM‐generated domain model with an expert‐constructed reference model using both qualitative expert assessments and quantitative metrics. Results show that LLMs achieve high precision in identifying entities and enumerations, whereas relationship and attribute extraction remain considerably less reliable and require systematic human review. The findings underscore the value of LLMs in accelerating early‐stage MDE tasks, while highlighting the necessity of human feedback to ensure semantic integrity. This work contributes a practical and extensible tool and empirical insights that inform future research into hybrid AI‐human modelling workflows and advanced LLM techniques for software modelling.