How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation-Reference-Cited by-同舟云学术

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How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation

Published:2024-09-11 Issue: Volume: Page:1223-1235
ISSN:
Container-title:Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
language:
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Author:

Zhang Cen¹^ORCID,Zheng Yaowen¹^ORCID,Bai Mingqiang²^ORCID,Li Yeting²^ORCID,Ma Wei¹^ORCID,Xie Xiaofei³^ORCID,Li Yuekang⁴^ORCID,Sun Limin²^ORCID,Liu Yang¹^ORCID

Affiliation:

1. Nanyang Technological University, Singapore, Singapore

2. Institute of Information Engineering at Chinese Academy of Sciences, Beijing, China / University of Chinese Academy of Sciences, Beijing, China

3. Singapore Management University, Singapore, Singapore

4. University of New South Wales, Sydney, Australia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650212.3680355

Reference63 articles.

1. FUDGE: fuzz driver generation at scale

2. Hopper: Interpretative Fuzzing for Libraries

3. Codellama. 2023. codellama-34b-instruct Model. https://huggingface.co/codellama/CodeLlama-34b-hf

4. Open Source Contributors. 2023. Fuzz Driver of Civetweb Project. https://github.com/civetweb/civetweb/blob/master/fuzztest/fuzzmain.c

5. Open Source Contributors. 2023. Fuzz Driver of Coraring Project. https://github.com/RoaringBitmap/CRoaring/blob/master/fuzz/croaring_fuzzer.c

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