Paolo Tonella

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Università del Salento

Switzerland

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Paolo Tonella is a Professor at Universita della Svizzera Italiana in Switzerland. His research focuses on safety in unmanned aerial vehicles, system-level attacks on deep learning models in autonomous vehicles, and approaches to enhance testing methodologies. Recent articles highlight his work on autonomous driving systems and the optimization of web test scripts through innovative techniques.

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Self-assessment Oracles for Anticipatory Testing

Open Date: 2019-01-01

Close Date: 2023-12-31

Paolo Tonella

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Recent Grants

Articles (21)

When uncertainty leads to unsafety: Empirical insights into the role of uncertainty in unmanned aerial vehicle safety

A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles

Parallelization in System-Level Testing: Novel Approaches to Manage Test Suite Dependencies

Adaptive Random Testing with Q-grams: The Illusion Comes True

STILE: A tool for optimizing E2E web test scripts parallelization

Reinforcement learning for online testing of autonomous driving systems: a replication and extension study

GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming

Boundary State Generation for Testing and Improvement of Autonomous Driving Systems

Focused Test Generation for Autonomous Driving Systems

Testing of Deep Reinforcement Learning Agents with Surrogate Models

Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks

Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks - RCR Report

Mind the Gap! A Study on the Transferability of Virtual Versus Physical-World Testing of Autonomous Driving Systems

Assessing the security of inter-app communications in android through reinforcement learning

Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems

Uncertainty quantification for deep neural networks: An empirical comparison and usage guidelines

In vivo test and rollback of Java applications as they are

Confidence‐driven weighted retraining for predicting safety‐critical failures in autonomous driving systems

Diabetes und Sport

Diabète et sport

An Empirical Validation of Oracle Improvement

Collaborators (8)

Robert Feldt

David Clark

ALESSIO GAMBI

Mariano Ceccato

Stefano Russo

Alessandro Marchetto

Valerio Terragni

Andrea Stocco

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