Deep neural networks (DNN) are being used in a wide range of applications including safety-
critical systems. Several DNN test generation approaches have been proposed to generate …

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 …

Modern statistics and machine learning typically involve large amounts of data coupled with
computationally intensive methods. In a predictive modeling context, one seeks models that …

Artificial intelligence (AI) technology and systems have been advancing rapidly. However,
ensuring the reliability of these systems is crucial for fostering public confidence in their use …

Deep learning techniques have revolutionized image classification by mimicking human
cognition and automating complex decision-making processes. However, the deployment of …

Active learning is a practical field of machine learning that automates the process of
selecting which data to label. Current methods are effective in reducing the burden of data …

This paper addresses the urgent need for messaging standards in the operational test and
evaluation (T&E) of machine learning (ML) applications, particularly in edge ML applications …

In the second of two panel discussion articles focused on the evolution of statistical
engineering (SE) as introduced by Roger Hoerl and Ronald Snee, a group of leading …

The data-intensive nature of machine learning (ML)-enabled systems introduces unique
challenges in test and evaluation. We present an overview of combinatorial coverage …

Machine learning models are increasingly used in practice. However, many machine
learning methods are sensitive to test or operational data that is dissimilar to training data …