Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

Similar to traditional software that is constantly under evolution, deep neural networks need
to evolve upon the rapid growth of test data for continuous enhancement (eg, adapting to …

Graph Neural Networks (GNNs) have achieved promising performance in a variety of
practical applications. Similar to traditional DNNs, GNNs could exhibit incorrect behavior that …

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 …

Although deep learning (DL) software has been pervasive in various applications, the
brittleness of deep neural networks (DNN) hinders their deployment in many tasks …

Testing deep learning-based systems is crucial but challenging due to the required time and
labor for labeling collected raw data. To alleviate the labeling effort, multiple test selection …

Code search is a common yet important activity of software developers. An efficient code
search model can largely facilitate the development process and improve the programming …

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 …

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 …