A Fine-Grained Evaluation of Mutation Operators for Deep Learning Systems: A Selective Mutation Approach-Reference-Cited by-同舟云学术

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A Fine-Grained Evaluation of Mutation Operators for Deep Learning Systems: A Selective Mutation Approach

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th Asia-Pacific Symposium on Internetware
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Author:

Wang Yichun¹^ORCID,Zhang Zhiyi²^ORCID,Yao Yongming³^ORCID,Huang Zhiqiu⁴^ORCID

Affiliation:

1. Nanjing University of Aeronautics and Astronautics, China

2. Nanjing University of Aeronautics and Astronautics, China and Collaborative Innovation Center of Novel Software Technology and Industrialization, China

3. Army Engineering of PLA, China

4. Nanjing University of Aeronautics and Astronautics, China and Ministry Key Laboratory for Safety-Critical Software Development and Verification, China

Funder

Natural Science Foundation of Jiangsu Province

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3609437.3609453

Reference30 articles.

1. Nicholas Carlini and David Wagner . 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp) . IEEE , 39–57. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). IEEE, 39–57.

2. Coverage-based quality metric of mutation operators for test suite improvement

3. Assessment of C++ object-oriented mutation operators: A selective mutation approach

4. Quality metrics for mutation testing with applications to WS-BPEL compositions

5. Selective mutation testing for concurrent code

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