Pre-trained models of code have achieved success in many important software engineering
tasks. However, these powerful models are vulnerable to adversarial attacks that slightly …

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of
code and play a vital role in supporting downstream automated software engineering tasks …

Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …

Deep neural networks (DNNs) are widely used in various application domains such as
image processing, speech recognition, and natural language processing. However, testing …

The increasing use of Machine Learning (ML) software can lead to unfair and unethical
decisions, thus fairness bugs in software are becoming a growing concern. Addressing …

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets
and increasing computational power, have become effective solutions to various critical …

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 …

Quality assurance of deep neural networks (DNNs) is crucial for the deployment of DNN-
based software, especially in mission-and safety-critical tasks. Inspired by structural white …

Deep Learning models have become an integrated component of modern software systems.
In response to the challenge of model design, researchers proposed Automated Machine …

As automated speech recognition (ASR) systems gain widespread acceptance, there is a
pressing need to rigorously test and enhance their performance. Nonetheless, the process …