Code models have made significant advancements in code intelligence by encoding
knowledge about programming languages. While previous studies have explored the …

Regularly testing deep learning-powered systems on newly collected data is critical to
ensure their reliability, robustness, and efficacy in real-world applications. This process is …

Large Language Models (LLMs) are widely used in many different domains, but because of
their limited interpretability, there are questions about how trustworthy they are in various …

Large language models (LLMs) have recently achieved significant success across various
application domains, garnering substantial attention from different communities …

Successful deployment of Deep Neural Networks (DNNs), particularly in safety-critical
systems, requires their validation with an adequate test set to ensure a sufficient degree of …

In this thesis, we introduce a testing framework designed to identify fault-revealing data in
Deep Neural Network (DNN) models and determine the causes of these failures. Given the …

Many optimization problems are inherently difficult and belong to a special class called NP-
hard. Thus, efficient algorithms, which we shall call integrated languages or simply …

The three-dimensional Heterogeneous T/R module utilizes three-dimensional
heterogeneous integration and has the characteristics of high integration and high power …