Prompt-Strategy-Driven SysML-v2 Artefact Generation Using Large Language Models for Model-Based Systems Engineering

Abstract

Large Language Models (LLMs) show growing potential for transforming natural-language engineering information into formal model-based artefacts. However, their reliability for SysML-v2 artefact generation remains insufficiently understood, particularly regarding prompt-strategy selection, model-dependent variability, and task-specific performance. This paper proposes a prompt-strategy-driven method for LLM-assisted SysML-v2 artefact generation in Model-Based Systems Engineering. The method is grounded in a systematic literature analysis and a Harvey-Balls-based assessment of existing approaches, which reveal gaps in reproducibility, prompt evaluation, and formal modelling support. The proposed workflow integrates input preparation, prompt-strategy selection, LLM selection, artefact generation, syntax validation, and quality evaluation. It is evaluated using a traction battery system case study across three representative modelling tasks: requirements generation, block definition modelling, and state-machine modelling. Four LLMs are compared using five prompt strategies and assessed through F1-score analysis and LLM-as-a-Judge evaluation. Within the investigated traction-battery case study, the observed artefact scores differed across prompt strategies and modelling tasks, and no single strategy achieved the highest observed score across all tasks. Overall, the findings indicate that LLMs can support early-stage SysML-v2 modelling as human-in-the-loop assistants, while expert validation remains necessary to ensure syntactic correctness, semantic consistency, and domain validity.