Data traceability in software development can connect different software artifacts to enhance
the observability of developer practices. In particular, traceability links between issues and …

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 …

Deep Learning (DL) is increasingly being integrated into Web applications through a
method known as" in-browser inference", where the DL processes occur directly within Web …

Accessible design focuses on enabling as many people as possible to access software
products and services, which has drawn extensive attention from software engineering …

Deep learning (DL) systems are complex component-based systems, which consist of core
program (code implementation and data), Python (language and interpreter), third-party …

Deep learning frameworks (DLFs) have been playing an increasingly important role in this
intelligence age since they act as a basic infrastructure for an increasingly wide range of AI …

While deep neural networks provide state-of-the-art solutions to a wide range of
programming language tasks, their effectiveness in dealing with foundational program …

The rapid advancement of Artificial Intelligence (AI) has led to its integration into various
areas, especially with Large Language Models (LLMs) significantly enhancing capabilities …

Code intelligence tools such as GitHub Copilot have begun to bridge the gap between
natural language and programming language. A frequent software development task is the …

Rapid advancements in edge devices have led to a large deployment of deep neural
network (DNN) based workloads. To utilize the resources at the edge effectively, many DNN …