Deep neural networks are at the forefront of machine learning research. However, despite
achieving impressive performance on complex tasks, they can be very sensitive: Small …

Machine learning techniques, especially deep learning, have achieved remarkable
breakthroughs over the past decade. At present, machine learning applications are …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

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 …

Deep Learning (DL) systems are key enablers for engineering intelligent applications due to
their ability to solve complex tasks such as image recognition and machine translation …

Deep neural networks (DNNs) have been deployed in a wide range of applications. We
introduce a DNN testing and debugging tool, called DeepConcolic, which is able to detect …

Methods for safety assurance suggested by the ISO 26262 automotive functional safety
standard are not sufficient for applications based on machine learning (ML). We provide a …

Over the past few years, convolutional neural networks (CNNs) have proved to reach
superhuman performance in visual recognition tasks. However, CNNs can easily be fooled …

In the last decade, deep learning techniques reached human-level performance in several
specific tasks as image recognition, object detection, and adaptive control. For this reason …

Automatic unit test generation that explores the input space and produces effective test
cases for given programs have been studied for decades. Many unit test generation tools …