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Large Language Models (LLM) and Generative Pre-trained Transformers (GPT), are reshaping the field of Software Engineering (SE).
Language models are few-shot learners
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Paweł Budzianowski and Ivan Vulić. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Natural language processing
KR1442 Chowdhary and KR Chowdhary. 2020 · 2020
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GPT-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti. 2020 · 2020
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GPT-3: What’s it good for?
Robert Dale. 2021 · 2021
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Cure: Code-aware neural machine translation for automatic program repair. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 1161–1173
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021 · 2021
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Understanding emails and drafting responses–An approach using GPT-3
Jonas Thiergart, Stefan Huber, and Thomas Übellacker. 2021 · 2021
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Exploring transformers in natural language generation: Gpt, bert, and xlnet
M Onat Topal, Anil Bas, and Imke van Heerden. 2021 · 2021
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Automatic code documentation generation using gpt-3. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–6
Junaed Younus Khan and Gias Uddin. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Cited alongside, same era.
Incorporating domain knowledge through task augmentation for front-end JavaScript code generation. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1533–1543
Sijie Shen, Xiang Zhu, Yihong Dong, Qizhi Guo, Yankun Zhen, and Ge Li. 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.
Mingyu Zong and Bhaskar Krishnamachari. 2022 · 2022
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
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Regulating ChatGPT and other large generative AI models. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency . 1112–1123
Philipp Hacker, Andreas Engel, and Marco Mauer. 2023 · 2023
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ChatGPT as a Software Development Tool: The Future of Development
Adam Hörnemalm. 2023 · 2023
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Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023a · 2023
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GPT understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2023b · 2023
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Towards human-bot collaborative software architecting with chatgpt. In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering . 279–285
Aakash Ahmad, Muhammad Waseem, Peng Liang, Mahdi Fahmideh, Mst Shamima Aktar, and Tommi Mikkonen. 2023 · 2023
Cited alongside, same era.
Is ChatGPT leading generative AI? What is beyond expectations?
Ömer Aydın and Enis Karaarslan. 2023 · 2023
Cited alongside, same era.
Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning
David Baidoo-Anu and Leticia Owusu Ansah. 2023 · 2023
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.
A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages
Alessio Buscemi. 2023 · 2023
Cited alongside, same era.
Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S Yu, and Lichao Sun. 2023 · 2023
Cited alongside, same era.
Self-collaboration Code Generation via ChatGPT
Yihong Dong, Xue Jiang, Zhi Jin, and Ge Li. 2023a · 2023
Cited alongside, same era.
CODEP: grammatical seq2seq model for general-purpose code generation. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis . 188–198
Yihong Dong, Ge Li, and Zhi Jin. 2023b
Cited in the paper.
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The Scope of ChatGPT in Software Engineering: A Thorough Investigation
Wei Ma, Shangqing Liu, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, and Yang Liu. 2023 · 2023
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Comparing Software Developers with ChatGPT: An Empirical Investigation
Nathalia Nascimento, Paulo Alencar, and Donald Cowan. 2023 · 2023
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023
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Is ChatGPT the Ultimate Programming Assistant–How far is it?
Haoye Tian, Weiqi Lu, Tsz On Li, Xunzhu Tang, Shing-Chi Cheung, Jacques Klein, and Tegawendé F Bissyandé. 2023 · 2023
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Navigating Complexity in Software Engineering: A Prototype for Comparing GPT-n Solutions
Christoph Treude. 2023 · 2023
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Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, and Douglas C Schmidt. 2023 · 2023
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