Software testing is an essential yet costly phase of the software development lifecycle. While
machine learning-based test suite optimization techniques have shown promise in reducing …

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 …

Quantum Neural Network (QNN) combines the deep learning (DL) principle with the
fundamental theory of quantum mechanics to achieve machine learning tasks with quantum …

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL
systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL …

The exponential growth of social media platforms, such as Facebook, Instagram, Youtube,
and TikTok, has revolutionized communication and content publication in human society …

Crowdsourcing Federated learning (CFL) is a new crowdsourcing development paradigm
for the Deep Neural Network (DNN) models, also called “software 2.0”. In practice, the …

Context: Testing techniques to ensure the quality of deep neural networks (DNNs) are
essential and crucial. However, the testing process can be inefficient due to a large number …

The increasing number of software applications incorporating machine learning (ML)
solutions has led to the need for testing techniques. However, testing ML software requires …

Abstract Recurrent Neural Network (RNN) is a typical feedback neural network, which is
particularly effective in processing time-series data tasks such as image description, text …

Deep neural networks (DNNs) have been widely deployed in software to address various
tasks (eg, autonomous driving, medical diagnosis). However, they can also produce …