Nargiz Humbatova, Gunel Jahangirova, Paolo Tonella:
Spectral Analysis of the Relation between Deep Learning Faults and Neural Activation Values. Proceedings of the IEEE International Conference on Software Testing (
ICST), 2024.
Ruben Grewal, Paolo Tonella, Andrea Stocco:
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification. Proceedings of the IEEE International Conference on Software Testing (
ICST), 2024.
Andréa Doreste, Matteo Biagiola, Paolo Tonella:
Adversarial Testing with Reinforcement Learning: A Case Study on Autonomous Driving. Proceedings of the IEEE International Conference on Software Testing (
ICST), 2024.
Matteo Biagiola, Andrea Stocco, Vincenzo Riccio, Paolo Tonella:
Two is better than one: digital siblings to improve autonomous driving testing. Empirical Software Engineering (
EMSE), 2024.
DOI
Michael Weiss, Paolo Tonella:
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks. ACM Transactions on Software Engineering and Methodology (
TOSEM), 2024.
DOI
Matteo Biagiola, Paolo Tonella:
Testing of Deep Reinforcement Learning Agents with Surrogate Models. ACM Transactions on Software Engineering and Methodology (
TOSEM), 2024.
DOI
Michele Pasqua, Mariano Ceccato, Paolo Tonella:
Hypertesting of Programs: Theoretical Foundation and Automated Test Generation. Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (
ICSE), 2024.
DOI
Michael Weiss, André García Gómez, Paolo Tonella:
Generating and detecting true ambiguity: a forgotten danger in DNN supervision testing. Empirical Software Engineering (
EMSE), 2023.
DOI
Antonia Bertolino, Guglielmo De Angelis, Breno Miranda, Paolo Tonella:
In vivo test and rollback of Java applications as they are. Journal of Software: Testing, Verification and Reliability (
STVR), 2023.
DOI
Tahereh Zohdinasab, Vincenzo Riccio, Paolo Tonella:
An Empirical Study on Low- and High-Level Explanations of Deep Learning Misbehaviours. Proceedings of the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (
ESEM), 2023.
DOI
Vincenzo Riccio, Paolo Tonella:
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study. Proceedings of the IEEE/ACM 45th International Conference on Software Engineering (
ICSE), 2023.
DOI
Tahereh Zohdinasab, Vincenzo Riccio, Paolo Tonella:
DeepAtash: Focused Test Generation for Deep Learning Systems. Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (
ISSTA), 2023.
DOI
Michael Weiss, Paolo Tonella:
Uncertainty quantification for deep neural networks: An empirical comparison and usage guidelines. Journal of Software: Testing, Verification and Reliability (
STVR), 2023.
DOI
Tahereh Zohdinasab, Vincenzo Riccio, Alessio Gambi, Paolo Tonella:
Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems. ACM Transactions on Software Engineering and Methodology (
TOSEM), 2023.
DOI
Andrea Romdhana, Alessio Merlo, Mariano Ceccato, Paolo Tonella:
Assessing the security of inter-app communications in android through reinforcement learning. Computer Security (
COSE), vol. 131, 2023.
DOI
Andrea Stocco, Brian Pulfer, Paolo Tonella:
Model vs system level testing of autonomous driving systems: a replication and extension study. Empirical Software Engineering (
EMSE), vol. 28, n. 3, 2023.
DOI
Andrea Stocco, Brian Pulfer, Paolo Tonella:
Mind the Gap! A Study on the Transferability of Virtual Versus Physical-World Testing of Autonomous Driving Systems. IEEE Transactions on Software Engineering (
TSE), vol. 49, n. 4, pp. 1928-1940, 2023.
DOI
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova, Paolo Tonella, Shin Yoo:
Repairing DNN Architecture: Are We There Yet?. Proceedings of the IEEE Conference on Software Testing, Verification and Validation (
ICST), pp. 234-245, 2023.
DOI
Sajad Khatiri, Sebastiano Panichella, Paolo Tonella:
Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights. Proceedings of the IEEE Conference on Software Testing, Verification and Validation (
ICST), pp. 281-292, 2023.
DOI
Michael Weiss, Paolo Tonella:
Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study). Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (
ISSTA), pp. 139-150, 2022.
DOI
Andrea Stocco, Paulo J. Nunes, Marcelo d'Amorim, Paolo Tonella:
ThirdEye: Attention Maps for Safe Autonomous Driving Systems. Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (
ASE), 2023.
DOI
Andrea Romdhana, Mariano Ceccato, Alessio Merlo, Paolo Tonella:
IFRIT: Focused Testing through Deep Reinforcement Learning. Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (
ICST), 2022.
DOI
Matteo Biagiola, Paolo Tonella:
Testing the Plasticity of Reinforcement Learning Based Systems. ACM Transactions on Software Engineering and Methodology (
TOSEM), 2022.
DOI
Andrea Romdhana, Alessio Merlo, Mariano Ceccato, Paolo Tonella:
Deep Reinforcement Learning for Black-Box Testing of Android Apps. ACM Transactions on Software Engineering and Methodology (
TOSEM), 2022.
DOI
Andrea Stocco, Paolo Tonella:
Confidence-driven Weighted Retraining for Predicting Safety-Critical Failures in Autonomous Driving Systems. Journal of Software: Evolution and Process (
JSEP), October, 2021.
DOI
Vincenzo Riccio, Nargiz Humbatova, Gunel Jahangirova, Paolo Tonella:
DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score. Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (
ASE), 2021.
DOI
Tahereh Zohdinasab, Vincenzo Riccio, Alessio Gambi, Paolo Tonella:
DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search. Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (
ISSTA), 2021.
DOI
Nargiz Humbatova, Gunel Jahangirova, Paolo Tonella:
DeepCrime: A Mutation Testing Tool for Deep Learning Systems. Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (
ISSTA), 2021.
DOI
Gunel Jahangirova, Andrea Stocco, Paolo Tonella:
Quality Metrics and Oracles for Autonomous Vehicles Testing. Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (
ICST), 2021.
DOI
Michael Weiss, Paolo Tonella:
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring. Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (
ICST), 2021.
DOI
Michael Weiss, Rwiddhi Chakraborty, Paolo Tonella:
A Review and Refinement of Surprise Adequacy. Proceedings of the International Workshop on Testing for Deep Learning and Deep Learning for Testing (
DeepTest), 2021.
DOI
Michael Weiss, Paolo Tonella:
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification. Proceedings of the IEEE International Conference on Software Testing, Verification and Validation, Tool Track (
ICST-Tool), 2021. Best tool paper award.
DOI
Valerio Terragni, Gunel Jahangirova, Paolo Tonella, Mauro Pezzè:
GAssert: A Fully Automated Tool to Improve Assertion Oracles. Proceedings of the IEEE International Conference on Software Engineering, Demonstrations Track (
ICSE-Demo), 2021.
DOI
Antonia Bertolino, Pietro Braione, Guglielmo De Angelis, Luca Gazzola, Fitsum Kifetew, Leonardo Mariani, Matteo Orrù, Mauro Pezzè, Roberto Pietrantuono, Stefano Russo, Paolo Tonella:
A Survey of Field-based Testing Techniques. ACM Computing Surveys (
CSUR), 2021.
DOI
Vincenzo Riccio, Gunel Jahangirova, Andrea Stocco, Nargiz Humbatova, Michael Weiss, Paolo Tonella:
Testing Machine Learning based Systems: A Systematic Mapping. Empirical Software Engineering (
EMSE), 2020.
DOI
Vincenzo Riccio, Paolo Tonella:
Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing. Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (
ESEC/FSE), 2020.
DOI
Valerio Terragni, Gunel Jahangirova, Mauro Pezzè, Paolo Tonella:
Evolutionary Improvement of Assertion Oracles. Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (
ESEC/FSE), 2020.
DOI
Nargiz Humbatova, Gunel Jahangirova, Gabriele Bavota, Vincenzo Riccio, Andrea Stocco, Paolo Tonella:
Taxonomy of Real Faults in Deep Learning Systems. Proceedings of the 42nd International Conference on Software Engineering (
ICSE), 2020.
DOI
Andrea Stocco, Michael Weiss, Paolo Tonella:
Misbehaviour Prediction for Autonomous Driving Systems. Proceedings of the 42nd International Conference on Software Engineering (
ICSE), 2020.
DOI
Gunel Jahangirova, Paolo Tonella:
An Empirical Evaluation of Mutation Operators for Deep Learning Systems. Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (
ICST), 2020. Best paper award.
DOI
Mariano Ceccato, Davide Corradini, Luca Gazzola, Fitsum Meshesha Kifetew, Leonardo Mariani, Matteo Orrù, Paolo Tonella:
A Framework for In-Vivo Testing of Mobile Applications. Proceedings of the 13th IEEE International Conference on Software Testing, Verification and Validation (
ICST), 2020.
DOI
Antonia Bertolino, Guglielmo De Angelis, Breno Miranda, Paolo Tonella:
Run Java Applications and Test Them In-Vivo Meantime. Proceedings of the 13th IEEE International Conference on Software Testing, Verification and Validation, Tool Track (
ICST-Tool), 2020.
DOI
Gunel Jahangirova, David Clark, Mark Harman, Paolo Tonella:
An Empirical Validation of Oracle Improvement. IEEE Transactions on Software Engineering (
TSE), 2019.
DOI
Mariano Ceccato, Luca Gazzola, Fitsum Meshesha Kifetew, Leonardo Mariani, Matteo Orrù, Paolo Tonella:
Toward In-Vivo Testing of Mobile Applications. Proceedings of the 1st International Workshop on Governing adaptive and Unplanned Systems of Systems (
GAUSS), 2019.
DOI
