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of 1 994
pro vyhledávání: '"Tonella"'
Autor:
Tehrani, Masoud Jamshidiyan, Kim, Jinhan, Foulefack, Rosmael Zidane Lekeufack, Marchetto, Alessandro, Tonella, Paolo
The advent of deep learning and its astonishing performance in perception tasks, such as object recognition and classification, has enabled its usage in complex systems, including autonomous vehicles. On the other hand, deep learning models are susce
Externí odkaz:
http://arxiv.org/abs/2412.04510
Autor:
Pasini, Samuele, Kim, Jinhan, Aiello, Tommaso, Lozoya, Rocio Cabrera, Sabetta, Antonino, Tonella, Paolo
Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements. However, LLMs struggle to generate accurate code, resulting, e.g., in attack detectors t
Externí odkaz:
http://arxiv.org/abs/2411.18216
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 quadratic number of distance computations as the number of test execu
Externí odkaz:
http://arxiv.org/abs/2410.17907
Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) conditions available in such simulators. To address this limitation, in thi
Externí odkaz:
http://arxiv.org/abs/2409.13661
Autor:
Thomas, Deepak-George, Biagiola, Matteo, Humbatova, Nargiz, Wardat, Mohammad, Jahangirova, Gunel, Rajan, Hridesh, Tonella, Paolo
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 robot. Correspondingly, novel approaches are needed to ensure that RL age
Externí odkaz:
http://arxiv.org/abs/2408.15150
The automated real-time recognition of unexpected situations plays a crucial role in the safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This paper evaluates different Bayesian uncertainty quantification methods
Externí odkaz:
http://arxiv.org/abs/2404.18573
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 search) for online testing of Deep Neural Network-enabled systems. Th
Externí odkaz:
http://arxiv.org/abs/2403.13729
Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding output
Externí odkaz:
http://arxiv.org/abs/2312.15302
Autor:
Biagiola, Matteo, Tonella, Paolo
Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However, assessing their dependability remains a critical concern. State-of-the-art ADS t
Externí odkaz:
http://arxiv.org/abs/2307.10590
Autor:
Stocco, Andrea, Willi, Alexandra, Starace, Luigi Libero Lucio, Biagiola, Matteo, Tonella, Paolo
Web test automation techniques employ web crawlers to automatically produce a web app model that is used for test generation. Existing crawlers rely on app-specific, threshold-based, algorithms to assess state equivalence. Such algorithms are hard to
Externí odkaz:
http://arxiv.org/abs/2306.07400