36 citations · 37 across the 8 of their papers we have counts for
12 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…
GAssert: A Fully Automated Tool to Improve Assertion Oracles
Valerio Terragni, Gunel Jahangirova, Paolo Tonella +1
This demo presents the implementation and usage details of GASSERT, the first tool to automatically improve assertion oracles. Assertion oracles are executable boolean expressions…
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…
An Empirical Study on Failed Error Propagation in Java Programs with Real Faults
Gunel Jahangirova, David Clark, Mark Harman +1
During testing, developers can place oracles externally or internally with respect to a method. Given a faulty execution state, i.e., one that differs from the expected one, an ora…
Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing
Vincenzo Riccio, Paolo Tonella
With the increasing adoption of Deep Learning (DL) for critical tasks, such as autonomous driving, the evaluation of the quality of systems that rely on DL has become crucial. Once…