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pro vyhledávání: '"A. Tonella"'
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
Autor:
Biagiola, Matteo, Tonella, Paolo
Publikováno v:
Trans. Softw. Eng. Methodol. 33, 3, Article 73 (March 2024)
Deep Reinforcement Learning (DRL) has received a lot of attention from the research community in recent years. As the technology moves away from game playing to practical contexts, such as autonomous vehicles and robotics, it is crucial to evaluate t
Externí odkaz:
http://arxiv.org/abs/2305.12751
Publikováno v:
Empir Software Eng 29, 72 (2024)
Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the generalizabilit
Externí odkaz:
http://arxiv.org/abs/2305.08060
Autor:
Weiss, Michael, Tonella, Paolo
Recent decades have seen the rise of large-scale Deep Neural Networks (DNNs) to achieve human-competitive performance in a variety of artificial intelligence tasks. Often consisting of hundreds of millions, if not hundreds of billion parameters, thes
Externí odkaz:
http://arxiv.org/abs/2304.02654