这篇论文提出一个好方法：用知识图谱加小模型自动修SysML v2语义错误，修复率飙到91%，相比传统编译器管不了语义错误，这招很实用。
本文提出一个人机协作框架，自动识别并修复SysML v2模型中的语义错误。框架结合微调的小语言模型（SLM）与领域知识图谱，其中知识图谱编码系统元素间的物理兼容性规则。通过系统引入领域违规，知识图谱指导生成合成训练数据，并在推理时增强模型以提供符合工程约束的修复建议。在车辆系统领域，对Qwen2.5-Coder-1.5B和DeepSeek-Coder-6.7B进行微调，输出统一diff补丁来定位故障并呈现候选修复。在1184个测试样本上，微调将语义故障修复率从低于3%提升至超过91%，且补丁输出减少令牌长度超过60%。
Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs
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.