34 citations · 39 across the 6 of their papers we have counts for
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cs.SE2023★ 4 cited
TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks
Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand +1
Successful deployment of Deep Neural Networks (DNNs) requires their validation with an adequate test set to ensure a sufficient degree of confidence in test outcomes. Although well…
cs.LG2023★ 34 cited
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
Zohreh Aghababaeyan, Manel Abdellatif, Mahboubeh Dadkhah +1
Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN model…