LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2

Zirui Li; Stephan Husung; Haoze Wang

cs.SEAug 22, 2025

LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2

Authors: Zirui Li, Stephan Husung, Haoze Wang

Organizations: Product and Systems Engineering Group Technische Universität Ilmenau Ilmenau, Germany

Abstract

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

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Jun 22, 2026 · cs.SEJ/K move · Enter open · S save

Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs

Haitham Al-Shami, Rohail Malik, Riku Ala-Laurinaho, Jari Vepsäläinen+1

SysML v2's textual syntax enables compiler-based validation of model structure and language conformance. However, semantic mistakes that preserve syntactic validity but violate domain rules cannot be detected through compilers. These errors can propagate through the design process and surface late as costly integration failures. This paper presents a human-in-the-loop framework for identifying and repairing such errors automatically. It combines a fine-tuned Small Language Model (SLM) with a domain knowledge graph encoding physical compatibility rules between system elements. The knowledge graph also guides the generation of synthetic training data by systematically introducing plausible domain violations, and augments the model at inference time to ground repair suggestions in valid engineering constraints. We demonstrate the framework using the vehicle systems domain, where the knowledge graph captures the relationships between the mechanical, electrical, fluid, and signal interfaces. Two SLMs, Qwen2.5-Coder-1.5B and DeepSeek-Coder-6.7B, are fine-tuned to output unified diff patches that localize faults and present candidate repairs for engineer review, preserving human judgment in the design process. Evaluation of 1,184 test samples shows that fine-tuning improves semantic fault repair from less than 3% to more than 91%, with patch-based output reducing token length by over 60%. The framework offers a practical path toward AI-assisted model verification that complements existing MBSE tools.

Fault Diagnosis Human-In-The-Loop Engineering

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Abstract

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