Zobrazeno 1 - 10
of 1 475
pro vyhledávání: '"A, 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, making them hard to understand and interpret by developers. On the other ha
Externí odkaz:
http://arxiv.org/abs/2412.18843
As the adoption of Deep Learning (DL) systems continues to rise, an increasing number of approaches are being proposed to test these systems, localise faults within them, and repair those faults. The best attestation of effectiveness for such techniq
Externí odkaz:
http://arxiv.org/abs/2412.16336
With the increased popularity of Deep Neural Networks (DNNs), increases also the need for tools to assist developers in the DNN implementation, testing and debugging process. Several approaches have been proposed that automatically analyse and locali
Externí odkaz:
http://arxiv.org/abs/2412.11304
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