The security guarantee of AI-enabled software systems (particularly using deep learning
techniques as a functional core) is pivotal against the adversarial attacks exploiting software …

As the impact of deep-learning-based software (DLSW) increases, automatic debugging
techniques for guaranteeing DLSW quality are becoming increasingly important. Information …

Over the past two decades, deep learning has received tremendous success in develo**
software systems across various domains. Deep learning frameworks have been proposed …

Efficiency is essential to support responsiveness wrt ever-growing datasets, especially for
Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred …

Context: Non native machine learning and deep learning (DL) developers face several
challenges in using DL frameworks owing to the issues persistent in DL documentation …

There is a growing interest in adopting Deep Learning (DL) given its superior performance
in many domains. However, modern DL frameworks such as TensorFlow often come with a …