A knowledge-graph-augmented fine-tuning method raises semantic fault repair success from under 3% to over 91% on 1,184 SysML v2 test samples while cutting output token length by over 60%.
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Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs
A knowledge-graph-augmented fine-tuning method raises semantic fault repair success from under 3% to over 91% on 1,184 SysML v2 test samples while cutting output token length by over 60%.