Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including
computer vision, system configuration, and question-answering. However, DNNs are …

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

Abstract Machine learning (ML) is extensively used in production-ready applications, calling
for mature engineering techniques to ensure robust development, deployment and …

Machine learning (ML)-based software systems are rapidly gaining adoption across various
domains, making it increasingly essential to ensure they perform as intended. This report …

Machine learning (ML) components are increasingly incorporated into software products for
end-users, but developers face challenges in transitioning from ML prototypes to products …

Artificial neural networks (ANNs) have become increasingly prevalent in various industries,
with applications in safety-critical domains such as image recognition, medical diagnosis …

Machine learning (ML) components are being added to more and more critical and impactful
software systems, but the software development process of real-world production systems …

Artificial Intelligence (AI) enabled systems are becoming capable and widespread. The use
of AI in critical system functions warrants a review of existing test and evaluation (T&E) tools …

Abstract Software Package Registries are an integral part of the Software Supply Chain,
acting as collaborative platforms that unite contributors, users, and packages, and …