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Improving Testing of Deep-Learning Systems.

The article focuses on the improvement of test effectiveness and quality of test data in deep learning systems. Topics of discussion include the combination of mutation testing using DeepMutation and differential testing using DeepXplore, iterations of models using data from the MNIST database, and...

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Published in:Communications of the ACM Vol. 67; no. 3; pp. 44 - 49
Main Authors: Deokuliar, Harsh, Sangwan, Raghvinder S., Badr, Yoaukim, Srinivasan, Satish M.
Format: Article
Published: Association for Computing Machinery Mar2024
Subjects:
Deep learning
Computer software testing
Data quality
Mutation testing of computer software
Iterative methods (Mathematics)
Artificial neural networks
Online Access:View this record in EBSCOhost
Summary:The article focuses on the improvement of test effectiveness and quality of test data in deep learning systems. Topics of discussion include the combination of mutation testing using DeepMutation and differential testing using DeepXplore, iterations of models using data from the MNIST database, and the discovery of errors in deep neural network (DNN) models.