Detecting Implementation Bugs in Graph Convolutional Network based Node Classifiers

Yiwei Wang, Wei Wang, Yujun Cai, Bryan Hooi, Beng Chin Ooi

Abstract: Graph convolutional networks (GCNs) have achieved state-of-the-art performance on the task of node classification. However, the performance of GCNs is prone to implementation bugs that do not explicitly produce compile-time or run-time errors but degrade their effectiveness heavily. These bugs are hard to detect, since the way in which the node attributes and graph structures contribute to the outputs is complicated, non-transparent, and not traceable by humans. To address this issue, we propose a systematic approach with formal justifications to detect implementation bugs in GCN based node classifiers. Our approach is based on the idea of Metamorphic Testing, which does not check input-output relations for a single input, but for input-output pairs. To speed up our approach, we design a pipeline system, which synchronizes the workload on CPUs and GPUs adaptively and processes them simultaneously. Our empirical study shows that our approach is able to identify over 80% of the synthetic mutants and two real-world bugs in GCN implementations. In addition, our pipeline system can achieve more than 10× speedup over the sequential system that leaves the CPU/GPU idle when using the other.