This article presents a comprehensive survey on test optimization in deep neural network
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …

3D point cloud applications have become increasingly prevalent in diverse domains,
showcasing their efficacy in various software systems. However, testing such applications …

Machine learning has achieved remarkable success across diverse domains. Nevertheless,
concerns about interpretability in black-box models, especially within Deep Neural Networks …

The widespread adoption of video-based applications across various fields highlights their
importance in modern software systems. However, in comparison to images or text, labelling …

With the breakthrough advancements of deep neural network technology in applications
such as image processing, autonomous driving, and speech recognition, the testing of deep …

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 …

The integration of artificial intelligence (AI) into mobile applications has significantly
transformed various domains, enhancing user experiences and providing personalized …

GNNs 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 …

In artificial intelligence testing, there is an increased focus on enhancing the efficiency of test
prioritization methods within deep learning systems. Subsequently, the DeepAbstraction …

Recently, there has been a surge in the adoption of deep learning (DL) techniques,
especially convolutional neural networks (CNNs), to perform hyperspectral image (HSI) …