34 citations · 120 across the 14 of their papers we have counts for
5 papers · 1 filter
Improving the Readability of Automatically Generated Tests using Large Language Models
Matteo Biagiola, Gianluca Ghislotti, Paolo Tonella
Search-based test generators are effective at producing unit tests with high coverage. However, such automatically generated tests have no meaningful test and variable names, makin…
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…
Adaptive Random Testing with Q-grams: The Illusion Comes True
Matteo Biagiola, Robert Feldt, Paolo Tonella
Adaptive Random Testing (ART) has faced criticism, particularly for its computational inefficiency, as highlighted by Arcuri and Briand. Their analysis clarified how ART requires a…
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…