Reversed Model Verification by Inferring Conceptual Models from Simulation Code

Abstract

Extracting high-level conceptual models from simulation code can benefit model validation and verification, system optimisation, and cross-disciplinary communication. However, conceptual models are often embedded within implementation details, making them difficult to access and interpret. This paper explores the feasibility of using Large Language Models (LLMs) to infer conceptual models from simulation code. We conduct a preliminary investigation on an agent-based simulation (Flee), demonstrating how LLMs can extract key structural, behavioural, and temporal elements. Our results suggest that LLMs can generate meaningful conceptual representations that align with expert-created models, offering potential support for model verification. However, we also identify limitations such as omissions and misinterpretations, highlighting the need for human oversight. While our study is based on a single example, it provides initial insights into the role of LLMs in conceptual model inference and their potential integration into simulation validation workflows.