Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study-Reference-Cited by-同舟云学术

Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study

Published:2024-02-06 Issue: Volume:33 Page:1-13
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
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Author:

Guo Qi¹^ORCID,Cao Junming²^ORCID,Xie Xiaofei³^ORCID,Liu Shangqing⁴^ORCID,Li Xiaohong¹^ORCID,Chen Bihuan²^ORCID,Peng Xin²^ORCID

Affiliation:

1. Tianjin University, Tianjin, China

2. Fudan University, Shanghai, China

3. Singapore Management University, Singapore, Singapore

4. Nanyang Technological University, Singapore, Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3623306

Reference45 articles.

1. Fatih Kadir Akin. 2023. Awesome Chatgpt Prompts. https://github.com/f/awesome-chatgpt-prompts.

2. Learning Autocompletion from Real-World Datasets

3. Impact of Peer Code Review on Peer Impression Formation: A Survey

4. Characteristics of Useful Code Reviews: An Empirical Study at Microsoft

5. Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. arXiv:2005.14165 [cs.CL]

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