In this work, we set out to conduct the first ground-truth empirical evaluation of state-of-the-
art DL fuzzers. Specifically, we first manually created an extensive DL bug benchmark …

Deep learning (DL) frameworks are widely used for neural network model training and
prediction in a lot of areas such as computer vision, natural language processing, medical …

Recently, Deep learning (DL) compilers have been widely developed to optimize the
deployment of DL models. These DL compilers transform DL models into high-level …

Deep Learning (DL) applications play a vital role in modern society. Bugs in DL libraries can
significantly impact a wide range of downstream DL applications, making it crucial to …

Fuzzing, a widely-used technique for bug detection, has seen advancements through Large
Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing …

Deep learning (DL) has attracted wide attention and has been widely deployed in recent
years. As a result, more and more research efforts have been dedicated to testing DL …

Bugs in Deep Learning (DL) libraries may affect almost all downstream DL applications, and
it is crucial to ensure the quality of such systems. It is challenging to generate valid input …

High scalability and low running costs have made fuzz testing the de facto standard for
discovering software bugs. Fuzzing techniques are constantly being improved in a race to …

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 …

The rapidly developing deep learning (DL) techniques have been applied in software
systems with various application scenarios. However, they could also pose new safety …