LeF‐MTP: Prioritizing GNN Test Cases by Fusing Model Uncertainty and Feature‐Space Confusability

ABSTRACT Graph Neural Networks (GNNs) are widely applied in 3D computer vision tasks. However, validating their reliability is limited by the high cost of labeling large‐scale test data. Existing test case prioritization techniques for deep learning often rely on single‐dimensional heuristics or mutation‐based analysis (e.g., GraphPrior, PCPrior). However, these methods often limit fault diversity or incur prohibitive computational overhead. To address these issues, this paper proposes LeF‐MTP, a multi‐dimensional test prioritization framework. Unlike these state‐of‐the‐art methods that depend on computationally intensive external mutations, LeF‐MTP directly exploits the model's internal cognitive states. The method characterizes test cases through two dimensions: the predictive uncertainty and feature‐space confusability. These cognitive features are fused via a Random Forest classifier to predict failure probabilities. The resulting priority list is then optimized by a diversity‐aware re‐ranking process based on feature clustering. We evaluated the framework on four 3D point cloud datasets (including ScanObjectNN and S3DIS) across four GNN architectures. Experimental results demonstrate that LeF‐MTP achieves higher fault detection rates than heuristic and mutation‐based baselines, with Average Percentage of Fault Detection (APFD) improvements ranging from 8.79% to 74.69%. Moreover, an efficiency analysis demonstrates that the proposed approach reduces feature generation time compared to mutation‐based methods, validating its practicality for GNN testing.