Examining Zero-Shot Vulnerability Repair with Large Language Models-Reference-Cited by-同舟云学术

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Examining Zero-Shot Vulnerability Repair with Large Language Models

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Symposium on Security and Privacy (SP)
language:
Short-container-title:

Author:

Pearce Hammond¹,Tan Benjamin²,Ahmad Baleegh¹,Karri Ramesh¹,Dolan-Gavitt Brendan¹

Affiliation:

1. New York University

2. University of Calgary

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10179215/10179280/10179420.pdf?arnumber=10179420

Reference52 articles.

1. SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

2. An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions;pearce,2021

3. An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

4. CURE: Code-Aware Neural Machine Translation for Automatic Program Repair

5. CoCoNuT: combining context-aware neural translation models using ensemble for program repair

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