Black-Box Testing of Deep Neural Networks through Test Case Diversity-Reference-Cited by-同舟云学术

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Black-Box Testing of Deep Neural Networks through Test Case Diversity

Published:2023-05-01 Issue:5 Volume:49 Page:3182-3204
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Aghababaeyan Zohreh¹^ORCID,Abdellatif Manel²^ORCID,Briand Lionel¹^ORCID,S Ramesh³^ORCID,Bagherzadeh Mojtaba¹^ORCID

Affiliation:

1. School of EECS, University of Ottawa, Ottawa, ON, Canada

2. Software and Information Technology Engineering Department, École de Technologie Supérieure, Montreal, QC, Canada

3. Department of Research and Development, General Motors, Warren, MI, USA

Funder

General Motors and Canada Research Chair

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

http://xplorestaging.ieee.org/ielx7/32/10124809/10041782.pdf?arnumber=10041782

Reference93 articles.

1. DeepGauge: multi-granularity testing criteria for deep learning systems

3. Correlations between deep neural network model coverage criteria and model quality

4. Towards Improved Testing For Deep Learning

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Distance-Aware Test Input Selection for Deep Neural Networks;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11

2. Validity Matters: Uncertainty‐Guided Testing of Deep Neural Networks;Software Testing, Verification and Reliability;2024-08-21

3. Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study;Empirical Software Engineering;2024-07-22

4. Evolutionary Generative Fuzzing for Differential Testing of the Kotlin Compiler;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

5. DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks;ACM Transactions on Software Engineering and Methodology;2024-06-27

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