The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
Open accessAbstract
The past decade has seen the great potential of applying deep neural network (DNN) based software to safety-critical scenarios, such as autonomous driving. Similar to traditional software, DNNs could exhibit incorrect behaviors, caused by hidden defects, leading to severe accidents and losses. In this paper, we propose DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs. To this end, we first propose a metamorphic mutation strategy to generate new semantically preserved tests, and leverage multiple extensible coverage criteria as feedback to guide the test generation. We further propose a seed selection strategy that combines both diversity-based and recency-based seed selection. We implement and incorporate 5 existing testing criteria and 4 seed selection strategies in DeepHunter. Large-scale experiments demonstrate that (1) our metamorphic mutation strategy is useful to generate new valid tests with the same semantics as the original seed, by up to a 98% validity ratio; (2) the diversity-based seed selection generally weighs more than recency-based seed selection in boosting the coverage and in detecting defects; (3) DeepHunter outperforms the state of the arts by coverage as well as the quantity and diversity of defects identified; (4) guided by corner-region based criteria, DeepHunter is useful to capture defects during the DNN quantization for platform migration.
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Cites
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Wang, Z., Yan, M., Chen, J., Liu, S., & Zhang, D. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3409761 · Cited by 167
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Self-citationDocTer: documentation-guided fuzzing for testing deep learning API functions
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Metadata
- Authors
- Xie, X., Ma, L., Juefei-Xu, F., Xue, M., Chen, H., Liu, Y., Zhao, J., Li, B., Yin, J., & See, S.
- Venue
- Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2019-07-10
- Pages
- 146-157
- OpenAlex
- W2957905354
- Cited by
- 426
- References
- 24
- FWCI
- 37.43
- Type
- conference paper
- Language
- en
- Open access
- green
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- Xie, X., Ma, L., Juefei-Xu, F., Xue, M., Chen, H., Liu, Y., Zhao, J., Li, B., Yin, J., & See, S. (2019). DeepHunter: a coverage-guided fuzz testing framework for deep neural networks. Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. https://doi.org/10.1145/3293882.3330579
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