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Automatic Fault Detection for Deep Learning Programs Using Graph Transformations.

, , , and . ACM Trans. Softw. Eng. Methodol., 31 (1): 14:1-14:27 (2022)

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Faults in deep reinforcement learning programs: a taxonomy and a detection approach., , , and . Autom. Softw. Eng., 29 (1): 8 (2022)Testing Feedforward Neural Networks Training Programs., and . CoRR, (2022)The Scent of Deep Learning Code: An Empirical Study., , , and . MSR, page 420-430. ACM, (2020)DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks., and . ICSME, page 454-458. IEEE, (2019)SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design., , , , and . ASE, page 94:1-94:13. ACM, (2022)Robustness assessment of hyperspectral image CNNs using metamorphic testing., , , , and . Inf. Softw. Technol., (2023)DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment., , , , and . CoRR, (2022)Automatic Fault Detection for Deep Learning Programs Using Graph Transformations., , , and . CoRR, (2021)Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases., , , , and . CoRR, (2021)Testing Feedforward Neural Networks Training Programs., and . ACM Trans. Softw. Eng. Methodol., 32 (4): 105:1-105:61 (July 2023)