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AI assistants for coding are on the rise.
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma. 2020 · 2009
Earlier work this paper cites.
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
Earlier work this paper cites.
Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, Online, 4582–4597
Xiang Lisa Li and Percy Liang. 2021 · 2021
Earlier work this paper cites.
Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2021 · 2021
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Introducing ChatGPT
Open AI. 2022 · 2022
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An Empirical Study of Code Smells in Transformer-based Code Generation Techniques. 71–82
Mohammed Latif Siddiq, Shafayat Majumder, Maisha Mim, Sourov Jajodia, and Joanna Cecilia da Silva Santos. 2022 · 2022
Earlier work this paper cites.
SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. In Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security (Singapore, Singapore) (MSR4P&S 2022) . Association for Computing Machinery, New York, NY, USA, 29–33
Mohammed Latif Siddiq and Joanna C. S. Santos. 2022 · 2022
Earlier work this paper cites.
Assessing the quality of GitHub copilot’s code generation. In Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering (Singapore, Singapore) (PROMISE 2022) . Association for Computing Machinery, New York, NY, USA, 62–71
Burak Yetistiren, Isik Ozsoy, and Eray Tuzun. 2022 · 2022
Earlier work this paper cites.
Multi-lingual Evaluation of Code Generation Models
Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, and Bing Xiang. 2023 · 2023
Earlier work this paper cites.
Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (<conf-loc>, <city>Toronto ON</city>, <country>Canada</country>, </conf-loc>) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 1136–1142
Paul Denny, Viraj Kumar, and Nasser Giacaman. 2023 · 2023
Cited alongside, same era.
Prompt Engineering with ChatGPT: A Guide for Academic Writers
L. Giray. 2023 · 2023
Cited alongside, same era.
Large Language Models for Code: Security Hardening and Adversarial Testing. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23) . ACM
Jingxuan He and Martin Vechev. 2023 · 2023
Cited alongside, same era.
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges
Jenny T. Liang, Chenyang Yang, and Brad A. Myers. 2023 · 2023
Cited alongside, same era.
LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluations. In 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR) . 588–592
Catherine Tony, Markus Mutas, Nicolás E. Díaz Ferreyra, and Riccardo Scandariato. 2023 · 2023
Later among the works it cites.
Small language models improve giants by rewriting their outputs
Giorgos Vernikos, Arthur Bražinskas, Jakub Adamek, Jonathan Mallinson, Aliaksei Severyn, and Eric Malmi. 2023 · 2023
Later among the works it cites.
Factcheck-GPT: End-to-End Fine-Grained Document-Level Fact-Checking and Correction of LLM Output
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, and Preslav Nakov. 2023 · 2023
Later among the works it cites.
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C. Schmidt. 2023 · 2023
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On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot
Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, and Gabriele Bavota. 2023 · 2023
Cited alongside, same era.
Prompt Engineering
OpenAI. 2023 · 2023
Cited alongside, same era.
Do Users Write More Insecure Code with AI Assistants?. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23) . ACM
Neil Perry, Megha Srivastava, Deepak Kumar, and Dan Boneh. 2023 · 2023
Cited alongside, same era.
Communicative Agents for Software Development
Chen Qian, Xin Cong, Wei Liu, Cheng Yang, Weize Chen, Yusheng Su, Yufan Dang, Jiahao Li, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
Cited alongside, same era.
Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants
Gustavo Sandoval, Hammond Pearce, Teo Nys, Ramesh Karri, Siddharth Garg, and Brendan Dolan-Gavitt. 2023 · 2023
Cited alongside, same era.
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023a
Cited in the paper.
AlphaCode 2 Technical Report
Shuang Li, Yi Xu, Anusha Krishna, Tianyu Chen, Taifu Wu, Peng Cao, and … 2023b
Cited in the paper.
Source Code Analysis Tools
OWASP. 2024b
Cited in the paper.
Later among the works it cites.
Balancing specialized and general skills in llms: The impact of modern tuning and data strategy
Zheng Zhang, Chen Zheng, Da Tang, Ke Sun, Yukun Ma, Yingtong Bu, Xun Zhou, and Liang Zhao. 2023 · 2023
Later among the works it cites.
GitHub Copilot
GitHub. 2024 · 2024
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Data-efficient Fine-tuning for LLM-based Recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, and Tat-Seng Chua. 2024 · 2024
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Snyk secures AI -
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Improving LLM Code Generation with Grammar Augmentation
Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, and Gagandeep Singh. 2024 · 2024
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