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Recent work in automated program repair (APR) proposes the use of reasoning and patch validation feedback to reduce the semantic gap between the LLMs and the code under analysis.
A systematic mapping study of web application testing
Vahid Garousi, Ali Mesbah, Aysu Betin-Can, and Shabnam Mirshokraie. 2013 · 2013
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VuRLE: Automatic Vulnerability Detection and Repair by Learning from Examples. In European Symposium on Research in Computer Security
Siqi Ma, Ferdian Thung, D. Lo, Cong Sun, and Robert H. Deng. 2017 · 2017
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CVE vulnerability CVE-2017-7601
NIST. 2023 · 2017
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Learning to Repair Software Vulnerabilities with Generative Adversarial Networks. In Neural Information Processing Systems
Jacob A. Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, and Peter Chin. 2018 · 2018
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DLFix: Context-based Code Transformation Learning for Automated Program Repair
Yi Li, Shaohua Wang, and Tien Nhut Nguyen. 2020 · 2020
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CoCoNuT: combining context-aware neural translation models using ensemble for program repair
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan. 2020 · 2020
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Beyond tests: Program vulnerability repair via crash constraint extraction
Xiang Gao, Bo Wang, Gregory J Duck, Ruyi Ji, Yingfei Xiong, and Abhik Roychoudhury. 2021 · 2021
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Cure: Code-aware neural machine translation for automatic program repair. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 1161–1173
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021 · 2021
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A comparative study of automatic program repair techniques for security vulnerabilities. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 196–207
Eduard Pinconschi, Rui Abreu, and Pedro Adão. 2021 · 2021
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Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code
Patrick Bareiß, Beatriz Souza, Marcelo d’Amorim, and Michael Pradel. 2022 · 2022
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Vul4J: a dataset of reproducible Java vulnerabilities geared towards the study of program repair techniques. In Proceedings of the 19th International Conference on Mining Software Repositories . 464–468
Quang-Cuong Bui, Riccardo Scandariato, and Nicolás E Díaz Ferreyra. 2022 · 2022
Cited alongside, same era.
Neural transfer learning for repairing security vulnerabilities in c code
Zimin Chen, Steve Kommrusch, and Martin Monperrus. 2022 · 2022
Cited alongside, same era.
InCoder: A Generative Model for Code Infilling and Synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022
Cited alongside, same era.
VulRepair: a T5-based automated software vulnerability repair. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 935–947
Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, and Dinh Phung. 2022 · 2022
Cited alongside, same era.
ExtractFix Benchmark Download Link
2023 · 2023
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Data quality for software vulnerability datasets. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 121–133
Roland Croft, M Ali Babar, and M Mehdi Kholoosi. 2023 · 2023
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Vulnerability Disclosure Cheat Sheet
OWASP. 2023 · 2023
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Examining Zero-Shot Vulnerability Repair with Large Language Models. In 2023 IEEE Symposium on Security and Privacy (SP) . IEEE Computer Society, 1–18
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt. 2022 · 2023
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Best practices for prompt engineering with OpenAI API
Jessica Shieh. 2023 · 2023
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How Effective Are Neural Networks for Fixing Security Vulnerabilities
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Jie Huang and Kevin Chen-Chuan Chang. 2022 · 2022
Cited alongside, same era.
Repairing Security Vulnerabilities Using Pre-trained Programming Language Models. In 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) . IEEE, 111–116
Kai Huang, Su Yang, Hongyu Sun, Chengyi Sun, Xuejun Li, and Yuqing Zhang. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022
Cited alongside, same era.
Program vulnerability repair via inductive inference. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis . 691–702
Yuntong Zhang, Xiang Gao, Gregory J Duck, and Abhik Roychoudhury. 2022 · 2022
Cited alongside, same era.
ChatGPT Plugins
OpenAI. 2023a
Cited in the paper.
OpenAI Codex
OpenAI. 2023b
Cited in the paper.
Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, and Sameena Shah. 2023 · 2023
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Automated program repair in the era of large pre-trained language models. In Proceedings of ICSE 2023. Association for Computing Machinery
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023 · 2023
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Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
Chunqiu Steven Xia and Lingming Zhang. 2023 · 2023
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Getting More out of Large Language Models for Proofs
Shizhuo Dylan Zhang, Talia Ringer, and Emily First. 2023 · 2023
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