1 citations · 1 across the 4 of their papers we have counts for
5 papers · 1 filter
ADEPT: A Unified Framework for Deep Learning Test Adequacy
Yidi Kao, Shawn Burnham, Tommi Rose Fahy +1
Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.g., neuron activation b…
Provably Lossless Acceleration of DNN Mutation Testing via Memoization
Ali Ghanbari, Ben Greenman, Sasan Tavakkol +1
Mutation analysis has recently reemerged in the context of deep neural networks (DNNs) as a promising, but notoriously costly, approach for assessing test dataset adequacy. Existin…
Using Fourier Analysis and Mutant Clustering to Accelerate DNN Mutation Testing
Ali Ghanbari, Sasan Tavakkol
Deep neural network (DNN) mutation analysis is a promising approach to evaluating test set adequacy. Due to the large number of generated mutants that must be tested on large datas…
On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clustering
Lauren Lyons, Ali Ghanbari
Mutation analysis of deep neural networks (DNNs) is a promising method for effective evaluation of test data quality and model robustness, but it can be computationally expensive,…
Mutation-based Fault Localization of Deep Neural Networks
Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad +1
Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, inclu…