Abstract:

To address the challenges of complex system modeling, including the complexity of model construction and the high learning cost of SysML2.0 syntax, an intelligent SysML2.0 model generation method based on a syntax-semantic dual-layer prompting strategy was proposed. At the semantic layer, a structured semantic prompt template was constructed to standardize the representation of system compositions, behaviors, and relationships. At the syntactic layer, three prompting strategies, namely zero-shot prompting, instruction prompting, and few-shot prompting, were designed to guide Large Language Models (LLMs) in generating composition diagram and activity diagram model codes conforming to SysML2.0 syntax specifications. Meanwhile, model complexity and generation accuracy metrics were established to systematically evaluate the generation performance under different prompting strategies, model complexities, and LLM conditions. Experimental results showed that the few-shot prompting strategy achieved nearly 100% generation accuracy under different complexity levels and across different LLMs, significantly outperforming zero-shot and instruction prompting strategies. Although the generation performance of all methods decreased as model complexity increased, the few-shot prompting strategy demonstrated better adaptability and stability. The results indicated that the few-shot-driven syntax-semantic hierarchical prompting mechanism effectively alleviated semantic hallucinations and structural errors in SysML2.0 model generation by LLMs, thereby improving model generation quality without requiring large-scale annotated datasets. A feasible pathway was thus provided for the transformation of Model-Based Systems Engineering (MBSE) from manual modeling to intelligent modeling.

Key words: prompts, model-based systems engineering, SysML2.0, intelligent generation, large language model