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Large language models (LLMs) have demonstrated notable proficiency in code generation, with numerous prior studies showing their promising capabilities in various development scenarios.
Improving the Gating Mechanism of Recurrent Neural Networks
Albert Gu, Caglar Gulcehre, Tom Le Paine, Matt Hoffman, and Razvan Pascanu. 2020 · 1910
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An empirical study on developer interactions in stackoverflow. In Proceedings of the 28th annual ACM symposium on applied computing . 1019–1024
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GPT-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti. 2020 · 2020
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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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, et al · 2021
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 2021 · 2021
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Recognizing bot activity in collaborative software development
Mehdi Golzadeh, Tom Mens, Alexandre Decan, Eleni Constantinou, and Natarajan Chidambaram. 2022 · 2022
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Security implications of large language model code assistants: A user study
Gustavo Sandoval, Hammond Pearce, Teo Nys, Ramesh Karri, Brendan Dolan-Gavitt, and Siddharth Garg. 2022 · 2022
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What is it like to program with artificial intelligence?
Advait Sarkar, Andrew D Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, and Ben Zorn. 2022 · 2022
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Natural Language Processing with Transformers, Revised Edition
Lewis Tunstall, Leandro Von Werra, and Thomas Wolf. 2022 · 2022
Cited alongside, same era.
Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts . 1–7
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022 · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming . 1–10
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
Cited alongside, same era.
Grounded copilot: How programmers interact with code-generating models
Shraddha Barke, Michael B James, and Nadia Polikarpova. 2023 · 2023
Cited alongside, same era.
Taking Flight with Copilot: Early Insights and Opportunities of AI-Powered Pair-Programming Tools
Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. 2023 · 2023
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023 · 2023
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James Prather, Brent N Reeves, Paul Denny, Brett A Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett Powell, James Finnie-Ansley, and Eddie Antonio Santos. 2023 · 2023
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The programmer’s assistant: Conversational interaction with a large language model for software development. In Proceedings of the 28th International Conference on Intelligent User Interfaces . 491–514
Steven I Ross, Fernando Martinez, Stephanie Houde, Michael Muller, and Justin D Weisz. 2023 · 2023
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Jiho Shin, Clark Tang, Tahmineh Mohati, Maleknaz Nayebi, Song Wang, and Hadi Hemmati. 2023 · 2023
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Cited alongside, same era.
Investigating Code Generation Performance of Chat-GPT with Crowdsourcing Social Data. In Proceedings of the 47th IEEE Computer Software and Applications Conference . 1–10
Yunhe Feng, Sreecharan Vanam, Manasa Cherukupally, Weijian Zheng, Meikang Qiu, and Haihua Chen. 2023 · 2023
Cited alongside, same era.
The GitHub Recent Bugs Dataset for Evaluating LLM-based Debugging Applications
Jae Yong Lee, Sungmin Kang, Juyeon Yoon, and Shin Yoo. 2023 · 2023
Cited alongside, same era.
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
Cited alongside, same era.
Understanding the Usability of AI Programming Assistants
Jenny T Liang, Chenyang Yang, and Brad A Myers. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks
Giriprasad Sridhara, Sourav Mazumdar, et al · 2023
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Software testing with large language model: Survey, landscape, and vision
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2023 · 2023
Later among the works it cites.
A study on robustness and reliability of large language model code generation
Li Zhong and Zilong Wang. 2023 · 2023
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DevGPT: Studying Developer-ChatGPT Conversations. In Proceedings of the International Conference on Mining Software Repositories (MSR 2024)
Tao Xiao, Christoph Treude, Hideaki Hata, and Kenichi Matsumoto. 2024 · 2024
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