Pre-trained large language models (LLMs) have recently emerged as a breakthrough
technology in natural language processing and artificial intelligence, with the ability to …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Deep Learning (DL) systems have received exponential growth in popularity and have
become ubiquitous in our everyday life. Such systems are built on top of popular DL …

Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need
for reliable systems. Generating valid input programs for fuzzing DL libraries is challenging …

There are increasing uses of deep learning (DL) compilers to generate optimized code,
boosting the runtime performance of DL models on specific hardware. Like their traditional …

Deep learning (DL) systems can make our life much easier, and thus are gaining more and
more attention from both academia and industry. Meanwhile, bugs in DL systems can be …

Deep Learning (DL) has gained wide attention in recent years. Meanwhile, bugs in DL
systems can lead to serious consequences, and may even threaten human lives. As a result …

Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Deep-learning (DL) compilers such as TVM and TensorRT are increasingly being used to
optimize deep neural network (DNN) models to meet performance, resource utilization and …

Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the
quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is …