Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis

Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is very costly, which could largely affect the efficiency of DNN testing, even the whole process of DNN development. To rel...

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Veröffentlicht in:	Proceedings / International Conference on Software Engineering S. 397 - 409
Hauptverfasser:	Wang, Zan, You, Hanmo, Chen, Junjie, Zhang, Yingyi, Dong, Xuyuan, Zhang, Wenbin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.05.2021
Schlagworte:	Companies Computer bugs Deep Learning Testing Deep Neural Network Label Labeling Mutation Neural networks Predictive models Test Prioritization Testing Training
ISBN:	1665402962, 9781665402965
ISSN:	1558-1225
Online-Zugang:	Volltext
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