In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …

Established approaches to assuring safety-critical systems and software are difficult to apply
to systems employing ML where there is no clear, pre-defined specification against which to …

Deep neural networks (DNN) have achieved tremendous development in the past decade.
While many DNN-driven software applications have been deployed to solve various tasks …

In this digital age, human dependency on technology in various fields has been increasing
tremendously. Torrential amounts of different electronic products are being manufactured …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Recent advances in deep neural networks (DNNs) have led to object detectors (ODs) that
can rapidly process pictures or videos, and recognize the objects that they contain. Despite …

We present Adapt, a new white-box testing technique for deep neural networks. As deep
neural networks are increasingly used in safety-first applications, testing their behavior …