97 citations · 440 across the 25 of their papers we have counts for
8 papers · 1 filter
Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems
Andrea Stocco, Brian Pulfer, Paolo Tonella
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment,…
DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
Vincenzo Riccio, Nargiz Humbatova, Gunel Jahangirova +1
Deep Learning (DL) components are routinely integrated into software systems that need to perform complex tasks such as image or natural language processing. The adequacy of the te…
DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search
Tahereh Zohdinasab, Vincenzo Riccio, Alessio Gambi +1
Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones. Several DL testing approaches have been recently proposed i…
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…
Deep Reinforcement Learning for Black-Box Testing of Android Apps
Andrea Romdhana, Alessio Merlo, Mariano Ceccato +1
The state space of Android apps is huge and its thorough exploration during testing remains a major challenge. In fact, the best exploration strategy is highly dependent on the fea…