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 …

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 …

Deep neural networks (DNN) have been shown to be useful in a wide range of applications.
However, they are also known to be vulnerable to adversarial samples. By transforming a …

In this paper, we consider the problem of formally verifying the safety of an autonomous
robot equipped with a Neural Network (NN) controller that processes LiDAR images to …

Recent advancements in radio frequency machine learning (RFML) have demonstrated the
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …

Deep learning has gained tremendous success and great popularity in the past few years.
However, deep learning systems are suffering several inherent weaknesses, which can …

Deep neural networks (DNNs) are increasingly being adopted for sensing and control
functions in a variety of safety and mission-critical systems such as self-driving cars …

Recurrent neural networks (RNNs) have been applied to a broad range of applications,
including natural language processing, drug discovery, and video recognition. Their …

Although deep learning has demonstrated astonishing performance in many applications,
there are still concerns about its dependability. One desirable property of deep learning …