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

Author:

Aghababaeyan Zohreh1ORCID,Abdellatif Manel2ORCID,Briand Lionel1ORCID,S Ramesh3ORCID,Bagherzadeh Mojtaba1ORCID

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

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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