An Empirical Study on AST-level mutation-based fuzzing techniques for JavaScript Engines-Reference-Cited by-同舟云学术

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An Empirical Study on AST-level mutation-based fuzzing techniques for JavaScript Engines

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

Author:

Tang Song¹^ORCID,Liu Shuang¹^ORCID,Wang Junjie¹^ORCID,Zhang Xiangwei¹^ORCID

Affiliation:

1. College of Intelligence and Computing, Tianjin University, China

Funder

Ministry of Industry and Information Technology Open Project

National Natural Science Foundation of China

Key Laboratory of Safety-Critical Software

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3609437.3609440

Reference40 articles.

1. 2022. American fuzzy lop.https://lcamtuf.coredump.cx/afl/ 2022. American fuzzy lop.https://lcamtuf.coredump.cx/afl/

2. 2022. Google Chrome. https://www.google.com/chrome/ 2022. Google Chrome. https://www.google.com/chrome/

3. 2022. Microsoft Edge. https://www.microsoft.com/en-us 2022. Microsoft Edge. https://www.microsoft.com/en-us

4. 2023. Antlr’s grammar list for different languages. https://github.com/antlr/grammars-v4 2023. Antlr’s grammar list for different languages. https://github.com/antlr/grammars-v4

5. Apple. 2022. Safari. https://www.apple.com/safari/ Apple. 2022. Safari. https://www.apple.com/safari/

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