23 citations · 25 across the 7 of their papers we have counts for
7 papers
Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing
Matteo Biagiola, Andrea Stocco, Vincenzo Riccio +1
Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simu…
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study
Vincenzo Riccio, Paolo Tonella
Testing Deep Learning (DL) based systems inherently requires large and representative test sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Te…
Does Road Diversity Really Matter in Testing Automated Driving Systems? -- A Registered Report
Stefan Klikovits, Vincenzo Riccio, Ezequiel Castellano +3
Background/Context. The use of automated driving systems (ADSs) in the real world requires rigorous testing to ensure safety. To increase trust, ADSs should be tested on a large se…
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…
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…
Taxonomy of Real Faults in Deep Learning Systems
Nargiz Humbatova, Gunel Jahangirova, Gabriele Bavota +3
The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance. In this paper we introduc…