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In this paper, we first report an exploratory study where three participants were instructed to use ChatGPT to implement a simple Web-based application.
An empirical evaluation of GitHub Copilot’s code suggestions
Nhan Nguyen and Sarah Nadi · 2022
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 · 2022
Earlier work this paper cites.
Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?
Owura Asare, Meiyappan Nagappan, and N. Asokan · 2023
Earlier work this paper cites.
Real-real-world programming with ChatGPT, 2023
Philip Guo · 2023
Earlier work this paper cites.
An evaluation of log parsing with ChatGPT
Van-Hoang Le and Hongyu Zhang · 2023
Earlier work this paper cites.
Refining ChatGPT-generated code: Characterizing and mitigating code quality issues
Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Tantithamthavorn, Li Li, Xuan-Bach Dinh Le, and David Lo · 2023
Earlier work this paper cites.
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
Cited alongside, same era.
Github copilot ai pair programmer: Asset or liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, and Zhen Ming (Jack) Jiang · 2023
Cited alongside, same era.
CodeCompose: A large-scale industrial deployment of AI-assisted code authoring
Vijayaraghavan Murali, Chandra Shekhar Maddila, Imad Ahmad, Michael Bolin, Daniel Cheng, Negar Ghorbani, Renuka Fernandez, and Nachiappan Nagappan · 2023
Cited alongside, same era.
The impact of AI on developer productivity: Evidence from GitHub Copilot
Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer · 2023
Cited alongside, same era.
Exploring the effectiveness of large language models in generating unit tests
Mohammed Latif Siddiq, Joanna C. S. Santos, Ridwanul Hasan Tanvir, Noshin Ulfat, Fahmid Al Rifat, and Vinicius Carvalho Lopes · 2023
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An analysis of the automatic bug fixing performance of ChatGPT
Dominik Sobania, Martin Briesch, Carol Hanna, and Justyna Petke · 2023
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ChatGPT prompt patterns for improving code quality, refactoring, requirements elicitation, and software design, 2023
Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, and Douglas C. Schmidt · 2023
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Automated program repair in the era of large pre-trained language models
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang · 2023
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Automated unit test improvement using large language models at meta
Nadia Alshahwan, Jubin Chheda, Anastasia Finegenova, Beliz Gokkaya, Mark Harman, Inna Harper, Alexandru Marginean, Shubho Sengupta, and Eddy Wang · 2024
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Ahmed R. Sadik, Antonello Ceravola, Frank Joublin, and Jibesh Patra · 2023
Cited alongside, same era.
Prompt engineering or fine tuning: An empirical assessment of large language models in automated software engineering tasks, 2023
Jiho Shin, Clark Tang, Tahmineh Mohati, Maleknaz Nayebi, Song Wang, and Hadi Hemmati · 2023
Cited alongside, same era.
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Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning, 2024
Mingyang Geng, Shangwen Wang, Dezun Dong, Haotian Wang, Ge Li, Zhi Jin, Xiaoguang Mao, and Xiangke Liao · 2024
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