1 citations · 2 across the 3 of their papers we have counts for
3 papers
Benchmarking Generative AI Models for Deep Learning Test Input Generation
Maryam, Matteo Biagiola, Andrea Stocco +1
Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets…
muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
Deepak-George Thomas, Matteo Biagiola, Nargiz Humbatova +4
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid rob…
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
Luca Giamattei, Matteo Biagiola, Roberto Pietrantuono +2
In a recent study, Reinforcement Learning (RL) used in combination with many-objective search, has been shown to outperform alternative techniques (random search and many-objective…