Software Engineering

Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis

This paper proposes a graph neural network framework to analyze assurance cases (structured safety argument documents) for link prediction between argument elements and detection of LLM-generated versus human-authored cases. The work reveals that LLM-generated assurance cases have distinct structural patterns and highlights limitations in GNN explanation methods.