4 papers
A Review and Refinement of Surprise Adequacy
Michael Weiss, Rwiddhi Chakraborty, Paolo Tonella
Surprise Adequacy (SA) is one of the emerging and most promising adequacy criteria for Deep Learning (DL) testing. As an adequacy criterion, it has been used to assess the strength…
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring
Michael Weiss, Paolo Tonella
Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inputs, such as images, videos, natural language texts or audio signals. Provided t…
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
Michael Weiss, Paolo Tonella
Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervi…
Misbehaviour Prediction for Autonomous Driving Systems
Andrea Stocco, Michael Weiss, Marco Calzana +1
Deep Neural Networks (DNNs) are the core component of modern autonomous driving systems. To date, it is still unrealistic that a DNN will generalize correctly in all driving condit…