Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study

作者：Xueqi Dang, Yinghua Li, Wei Ma, Yuejun Guo, Qiang Hu, Mike Papadakis, Maxime Cordy, Yves Le Traon · 发表于：Empirical Software Engineering · 年份：2024 · DOI：10.1007/s10664-024-10515-y · 被引用次数：8 · 研究领域：Advanced Graph Neural Networks、Bayesian Modeling and Causal Inference、Explainable Artificial Intelligence (XAI)

Abstract Graph Neural Networks (GNNs) have gained prominence in various domains, such as social network analysis, recommendation systems, and drug discovery, due to their ability to model complex relationships in graph-structured data. GNNs can exhibit incorrect behavior, resulting in severe consequences. Therefore, testing is necessary and pivotal. However, labeling all test inputs for GNNs can be prohibitively costly and time-consuming, especially when dealing with large and complex graphs. In response to these challenges, test selection has emerged as a strategic approach to alleviate labeling expenses. The objective of test selection is to select a subset of tests from the complete test set. While various test selection techniques have been proposed for traditional deep neural networks (DNNs), their adaptation to GNNs presents unique challenges due to the distinctions between DNN and GNN test data. Specifically, DNN test inputs are independent of each other, whereas GNN test inputs (nodes) exhibit intricate interdependencies. Therefore, it remains unclear whether DNN test selection approaches can perform effectively on GNNs. To fill the gap, we conduct an empirical study that systematically evaluates the effectiveness of various test selection methods in the context of GNNs, focusing on three critical aspects: 1) Misclassification detection : selecting test inputs that are more likely to be misclassified; 2) Accuracy estimation : selecting a small set of tests to precisel...