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 …

Automatic detection of software bugs is a critical task in software security. Many static tools
that can help detect bugs have been proposed. While these static bug detectors are mainly …

DL frameworks are the basis of constructing all DL programs and models, and thus their
bugs could lead to the unexpected behaviors of any DL program or model relying on them …

In recent years, deep learning has become a hot research topic. Although it achieves
incredible positive results in some scenarios, bugs inside deep learning software can …

Deep learning (DL) techniques attract people from various fields with superior performance
in making progressive breakthroughs. To ensure the quality of DL techniques, researchers …

Abstract Context: Deep Learning (DL) frameworks enable developers to build DNN models
without learning the underlying algorithms and models. While some of these DL-based …

Deep learning (DL) libraries reduce the barriers to the DL model construction. In DL
libraries, various building blocks are DL operators with different functionality, responsible for …

The application of machine learning (ML) libraries has tremendously increased in many
domains, including autonomous driving systems, medical, and critical industries …

Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (eg,
Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software …

The rapid escalation of applying Machine Learning (ML) in various domains has led to
paying more attention to the quality of ML components. There is then a growth of techniques …