Virtually any software running on a computer has been processed by a compiler or a
compiler-like tool. Because compilers are such a crucial piece of infrastructure for building …

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 …

Mobile apps are ubiquitous, operate in complex environments and are developed under the
time-to-market pressure. Ensuring their correctness and reliability thus becomes an …

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 …

In recent years, coverage-based greybox fuzzing has proven itself to be one of the most
effective techniques for finding security bugs in practice. Particularly, American Fuzzy Lop …

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 …

Device drivers are an essential part in modern Unix-like systems to handle operations on
physical devices, from hard disks and printers to digital cameras and Bluetooth speakers …

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 …

Compilers should not crash and they should not miscompile applications. Random testing is
an effective method for finding compiler bugs that have escaped other kinds of testing. This …

Fuzzing is a practical, widely-deployed technique to find bugs in complex, real-world
programs like JavaScript engines. We observed, however, that existing fuzzing approaches …