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- research-articleJuly 2024
Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study
Empirical Software Engineering (KLU-EMSE), Volume 29, Issue 5Sep 2024https://doi.org/10.1007/s10664-024-10515-yAbstractGraph 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 ...
- research-articleJune 2024
Test Input Prioritization for 3D Point Clouds
ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 33, Issue 5Article No.: 132, Pages 1–44https://doi.org/10.1145/36436763D point cloud applications have become increasingly prevalent in diverse domains, showcasing their efficacy in various software systems. However, testing such applications presents unique challenges due to the high-dimensional nature of 3D point cloud ...
- research-articleApril 2024
Test Input Prioritization for Graph Neural Networks
IEEE Transactions on Software Engineering (ISOF), Volume 50, Issue 6June 2024, Pages 1396–1424https://doi.org/10.1109/TSE.2024.3385538GNNs have shown remarkable performance in a variety of classification tasks. The reliability of GNN models needs to be thoroughly validated before their deployment to ensure their accurate functioning. Therefore, effective testing is essential for ...
- research-articleJanuary 2024
Test Input Prioritization for Machine Learning Classifiers
IEEE Transactions on Software Engineering (ISOF), Volume 50, Issue 3March 2024, Pages 413–442https://doi.org/10.1109/TSE.2024.3350019Machine learning has achieved remarkable success across diverse domains. Nevertheless, concerns about interpretability in black-box models, especially within Deep Neural Networks (DNNs), have become pronounced in safety-critical fields like healthcare and ...
- research-articleNovember 2023
GraphPrior: Mutation-based Test Input Prioritization for Graph Neural Networks
ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 33, Issue 1Article No.: 22, Pages 1–40https://doi.org/10.1145/3607191Graph Neural Networks (GNNs) have achieved promising performance in a variety of practical applications. Similar to traditional DNNs, GNNs could exhibit incorrect behavior that may lead to severe consequences, and thus testing is necessary and crucial. ...