Abstract Machine learning and deep learning models are commonly developed using
programming languages such as Python, C++, or R and deployed as web apps delivered …

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

An increasing number of software applications incorporate machine learning (ML) solutions
for cognitive tasks that statistically mimic human behaviors. To test such software …

Over the years, researchers capitalized on the repetitiveness of software changes to
automate many software evolution tasks. Despite the extraordinary rise in popularity of …

AI applications often use ML/DL (Machine Learning/Deep Learning) models to implement
specific AI tasks. As application developers usually are not AI experts, they often choose to …

Deep Learning (DL) libraries have significantly impacted various domains in computer
science over the last decade. However, developers often face challenges when using the …

Deep learning (DL) has been widely applied to many domains. Unique challenges in
engineering DL systems are posed by the programming paradigm shift from traditional …

The development and training of deep learning models have become increasingly costly
and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for …

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) techniques are increasingly being incorporated in critical software
systems today. DL software is buggy too. Recent work in SE has characterized these bugs …