Deephunter: A coverage-guided fuzz testing framework for deep neural networks

Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Minhui Xue, Hongxu Chen, Yang Liu, Jianjun Zhao, Bo Li, Jianxiong Yin, Simon See

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis
EditorsDongmei Zhang, Anders Moller
PublisherAssociation for Computing Machinery, Inc
Pages158-168
Number of pages11
ISBN (Electronic)9781450362245
DOIs
Publication statusPublished - Jul 10 2019
Event28th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2019 - Beijing, China
Duration: Jul 15 2019Jul 19 2019

Publication series

NameISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis

Conference

Conference28th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2019
CountryChina
CityBeijing
Period7/15/197/19/19

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Cite this

Xie, X., Ma, L., Juefei-Xu, F., Xue, M., Chen, H., Liu, Y., ... See, S. (2019). Deephunter: A coverage-guided fuzz testing framework for deep neural networks. In D. Zhang, & A. Moller (Eds.), ISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 158-168). (ISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis). Association for Computing Machinery, Inc. https://doi.org/10.1145/3293882.3330579