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In software development, the predominant emphasis on functionality often supersedes security concerns, a trend gaining momentum with AI-driven automation tools like GitHub Copilot.
Bertscore: Evaluating text generation with bert
Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2019 · 1904
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How to avoid making a billion-dollar mistake: Type-safe data plane programming with SafeP4
Eichholz, M.; Campbell, E.; Foster, N.; Salvaneschi, G.; and Mezini, M. 2019 · 1906
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Bleu: a method for automatic evaluation of machine translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. 2004 · 2004
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ReDeBug: finding unpatched code clones in entire os distributions
Jang, J.; Agrawal, A.; and Brumley, D. 2012 · 2012
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Contextual markov decision processes
Hallak, A.; Di Castro, D.; and Mannor, S. 2015 · 2015
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Beam search strategies for neural machine translation
Freitag, M.; and Al-Onaizan, Y. 2017 · 2017
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Vuddy: A scalable approach for vulnerable code clone discovery
Kim, S.; Woo, S.; Lee, H.; and Oh, H. 2017 · 2017
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Proximal policy optimization algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
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Using safety properties to generate vulnerability patches
Huang, Z.; Lie, D.; Tan, G.; and Jaeger, T. 2019 · 2019
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Microsoft wants to apply AI to the entire application developer lifecycle
Protalinski, E. 2019. accessed: June 2020 · 2019
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Zero: Memory optimizations toward training trillion parameter models
Rajbhandari, S.; Rasley, J.; Ruwase, O.; and He, Y. 2020 · 2020
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{ \{ MVP } \} : Detecting Vulnerabilities using { \{ Patch-Enhanced } \} Vulnerability Signatures
Xiao, Y.; Chen, B.; Yu, C.; Xu, Z.; Yuan, Z.; Li, F.; Liu, B.; Liu, Y.; Huo, W.; Zou, W.; et al. 2020 · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M.; Tworek, J.; Jun, H.; Yuan, Q.; Pinto, H. P. d. O.; Kaplan, J.; Edwards, H.; Burda, Y.; Joseph, N.; Brockman, G.; et al. 2021 · 2021
Cited alongside, same era.
Vuldeelocator: a deep learning-based fine-grained vulnerability detector
Li, Z.; Zou, D.; Xu, S.; Chen, Z.; Zhu, Y.; and Jin, H. 2021 · 2021
Cited alongside, same era.
Automatic Program Repair with OpenAI’s Codex: Evaluating QuixBugs
Prenner, J. A.; and Robbes, R. 2021 · 2021
Cited alongside, same era.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
Wang, Y.; Wang, W.; Joty, S.; and Hoi, S. C. 2021 · 2021
Deep TabNine
2023 · 2023
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Towards human-bot collaborative software architecting with chatgpt
Ahmad, A.; Waseem, M.; Liang, P.; Fahmideh, M.; Aktar, M. S.; and Mikkonen, T. 2023 · 2023
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2023 CWE Top 25 Most Dangerous Software Weaknesses
CISA. ???? · 2023
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Controlling large language models to generate secure and vulnerable code
He, J.; and Vechev, M. 2023 · 2023
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Large Language Models and Simple, Stupid Bugs
Jesse, K.; Ahmed, T.; Devanbu, P. T.; and Morgan, E. 2023 · 2023
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CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code Authoring
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Cited alongside, same era.
Neural transfer learning for repairing security vulnerabilities in c code
Chen, Z.; Kommrusch, S.; and Monperrus, M. 2022 · 2022
Cited alongside, same era.
VulRepair: a T5-based automated software vulnerability repair
Fu, M.; Tantithamthavorn, C.; Le, T.; Nguyen, V.; and Phung, D. 2022 · 2022
Cited alongside, same era.
GitHub Copilot
GitHub. 2022 · 2022
Cited alongside, same era.
Asleep at the keyboard? assessing the security of github copilot’s code contributions
Pearce, H.; Ahmad, B.; Tan, B.; Dolan-Gavitt, B.; and Karri, R. 2022a · 2022
Cited alongside, same era.
Security Implications of Large Language Model Code Assistants: A User Study
Sandoval, G.; Pearce, H.; Nys, T.; Karri, R.; Dolan-Gavitt, B.; and Garg, S. 2022 · 2022
Cited alongside, same era.
{ \{ MOVERY } \} : A Precise Approach for Modified Vulnerable Code Clone Discovery from Modified { \{ Open-Source } \} Software Components
Woo, S.; Hong, H.; Choi, E.; and Lee, H. 2022 · 2022
Cited alongside, same era.
Program vulnerability repair via inductive inference
Zhang, Y.; Gao, X.; Duck, G. J.; and Roychoudhury, A. 2022 · 2022
Cited alongside, same era.
Murali, V.; Maddila, C.; Ahmad, I.; Bolin, M.; Cheng, D.; Ghorbani, N.; Fernandez, R.; and Nagappan, N. 2023 · 2023
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Codegen2: Lessons for training llms on programming and natural languages
Nijkamp, E.; Hayashi, H.; Xiong, C.; Savarese, S.; and Zhou, Y. 2023 · 2023
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CISA, FBI, NSA, and International Partners Warn Organizations of Top Routinely Exploited Cybersecurity Vulnerabilities
NSA. 2022 · 2023
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Examining Zero-Shot Vulnerability Repair with Large Language Models
Pearce, H.; Tan, B.; Ahmad, B.; Karri, R.; and Dolan-Gavitt, B. 2022b · 2023
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The programmer’s assistant: Conversational interaction with a large language model for software development
Ross, S. I.; Martinez, F.; Houde, S.; Muller, M.; and Weisz, J. D. 2023 · 2023
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Executive Order on Improving the Nation’s Cybersecurity
WhiteHouse. 2023 · 2023
Later among the works it cites.