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 …

MLIR (Multi-Level Intermediate Representation) compiler infrastructure has gained
popularity in recent years to support the construction of many compilers. Instead of …

Deep learning has been widely adopted to tackle various code-based tasks by building
deep code models based on a large amount of code snippets. While these deep code …

Adversarial examples are important to test and enhance the robustness of deep code
models. As source code is discrete and has to strictly stick to complex grammar and …

Deep learning (DL) techniques have attracted much attention in recent years and have been
applied to many application scenarios. To improve the performance of DL models regarding …

With the increasing use of intelligent systems in various domains such as self-driving cars,
robotics, and smart cities, it is crucial to ensure the quality of intelligent systems for their …

Data-augmented deep learning models are widely used in real-world applications.
However, many state-of the-art loss-based or coverage-based fuzzing techniques fail to …

Fairness has been a critical issue that a ects the adoption of deep learning models in real
practice. To improve model fairness, many existing methods have been proposed and …

Many test coverage metrics have been proposed to measure the deep neural network
(DNN) testing effectiveness, including structural coverage and nonstructural coverage …