Observable Consistency Checking across Requirements and Models

Model-Driven Development (MDD) promises productivity gains in software development. However, its adoption remains limited due to persistent challenges in maintaining consistency, both between requirements and the models derived from them, and across the models themselves. Heterogeneous requirements and models from multiple disciplines, such as software, mechanical, or electrical engineering, often overlap in describing a system. They use different terminology and value ranges, which potentially leads to misalignments and contradictions. Existing syntactic methods capture only structural differences, while constraint-based approaches struggle to integrate heterogeneous artefacts. We address this challenge through observables: domain properties constrained by requirements and models, which serve as a unifying abstraction for consistency. We present an ontology-driven framework that leverages retrieval-augmented generation and large language models (LLMs) to automatically extract observables and constraints from heterogeneous artefacts. The extracted observables are harmonised via ontology construction, and the constraints are compiled into solver-ready formulas. This enables observable-centric semantic consistency checks across requirements and models, ensuring a consistent and accurate representation of data. It helps engineers identify inconsistencies earlier and reduces manual effort in consistency analysis. Evaluation on synthetic datasets and an industrial automotive case study demonstrates the feasibility and scalability from small sets to thousands of requirement.