MAS4SysML: A Multi-Agent Framework for SysML v2 Model Generation from Natural Language

Summary

This protocol presents MAS4SysML, a multi-agent approach that automatically generates SysML v2 code through coordinated task division, requiring few repair iterations and significantly reducing manual modeling time while improving system modeling efficiency.

Abstract

Automatically generating accurate SysML models from natural-language requirements can substantially accelerate the adoption of Model-Based Systems Engineering (MBSE) in complex system development. However, using large language models (LLMs) to generate model code often fails to meet the strict syntactic constraints of formal modeling languages, and consistently ensuring semantic alignment between generated models and requirements remains challenging. To address these challenges, this paper presents MAS4SysML, a multi-agent collaborative framework for SysML v2 code generation that improves syntactic correctness and semantic consistency under a constrained repair budget. The framework decomposes a modeling task into hierarchical subtasks, formalizes them as structured task cards, and generates model code in a bottom-up manner. During generation, an official validation environment is used for syntax diagnostics; after completion, the framework verifies semantic consistency between the code and the task cards. If syntax or semantic validation fails, the framework iteratively repairs and revalidates the code within a predefined repair budget, guided by diagnostic feedback, until the validation criteria are satisfied or the budget is exhausted. To evaluate the proposed method, we construct a SysML v2 dataset spanning five core task types—requirements, use cases, structure, parametrics, and state machines—and conduct comparative experiments. Results show that MAS4SysML reduces the average syntax error rate to 2.63, increases semantic similarity to 0.91, and outperforms existing code-generation methods overall.

Introduction

MBSE has become a key methodology for requirements analysis, system architecture design, and verification planning in the development of complex equipment in domains such as aviation and aerospace¹. Using unified modeling languages such as SysML as the modeling backbone, information—including requirements, structure, behavior, and constraints—can be organized into a coherent model framework, improving process structure and the efficiency of cross-disciplinary collaboration². However, as system scale continues to grow, the number of models that must be developed increases accordingly, leading to a sustained ri....

Protocol

The code generation process of the MAS4SysML framework is summarized in Supplementary File 1. It should be noted that this study does not aim to achieve the one-shot generation of a complete system model from natural language with strict cross-view consistency, including requirements, structure, parametrics, and behavior. Instead, the protocol focuses on generating several representative types of SysML v2 view code.

Phase I: Task analysis
The workflow begins with task parsing. The system provides the natural-language modeling intent to the Task Structure Generation Agent, which outputs a task-card set. ....

Results

Baseline model evaluation
We first selected several mainstream LLMs and conducted preliminary performance tests using direct model-to-code generation, including CodeX(175B)¹⁹, CodeGen-Mono(16.1B)²⁰, PaLM Coder(62B)²¹, Alphacode(1.1B)²², Incoder(6.7B)²³, and code-davinci-002(175B)²⁴. As shown in Table 2, code-davinci-002(175B)

Discussion

We propose MAS4SysML, a multi-agent collaborative framework for semi-automated SysML v2 model code generation. The framework consists of four functionally complementary agents. During generation, it (i) hierarchically decomposes natural-language modeling requirements using a task-tree–based structure and formalizes them into structured task cards, and (ii) generates SysML v2 model code in a bottom-up manner guided by the constraints and dependency relations specified in these cards. Throughout generation, a syntax .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
LangChain	LangChain (open-source project)	v1.0.8; https://github.com/langchain-ai/langchain	Framework for LLM interaction and agent orchestration
LangGraph	LangChain (open-source project)	v1.0.3; https://github.com/langchain-ai/langgraph	Multi-agent workflow execution framework
Python	Python Software Foundation	3.10.x; https://www.python.org/downloads/release/python-3100/	Main programming language for MAS4SysML implementation
SysML v2 Pilot Implementation	Object Management Group (OMG)	(provide release/tag version); https://github.com/Systems-Modeling/SysML-v2-Pilot-Implementation	Used for syntax validation and model parsing