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GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions.
Dimensions in Program Synthesis. In Proceedings of the 12th International ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming (Hagenberg, Austria) (PPDP ’10) . Association for Computing Machinery, New York, NY, USA, 13–24
Sumit Gulwani. 2010 · 2010
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REFLECTIONS Tools from the Education Industry
Deepak Kumar. 2018 · 2018
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Language Models Are Few-Shot Learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS’20) . Curran Associates Inc., Red Hook, NY, USA, Article 159, 25 pages
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 · 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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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21) . Association for Computing Machinery, New York, NY, USA, 610–623
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang. 2021 · 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, 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
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Avoiding the Turing Tarpit: Learning Conversational Programming by Starting from Code’s Purpose. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 61, 15 pages
Kathryn Cunningham, Barbara J. Ericson, Rahul Agrawal Bejarano, and Mark Guzdial. 2021 · 2021
Earlier work this paper cites.
Natural language-guided programming. In Proceedings of the 2021 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software . 39–55
Geert Heyman, Rafael Huysegems, Pascal Justen, and Tom Van Cutsem. 2021 · 2021
Cited alongside, same era.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
Cited alongside, same era.
A Survey of Automatic Code Generation from Natural Language
Jiho Shin and Jaechang Nam. 2021 · 2021
Cited alongside, same era.
Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models
Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli. 2021 · 2021
The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. In Australasian Computing Education Conference (Virtual Event, Australia) (ACE ’22) . Association for Computing Machinery, New York, NY, USA, 10–19
James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, and James Prather. 2022 · 2022
Closest in time.
Is GitHub Copilot a Substitute for Human Pair-programming? An Empirical Study. In 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) . 319–321
Saki Imai. 2022 · 2022
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Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22) . Association for Computing Machinery, New York, NY, USA, Article 386, 19 pages
Ellen Jiang, Edwin Toh, Alejandra Molina, Kristen Olson, Claire Kayacik, Aaron Donsbach, Carrie J Cai, and Michael Terry. 2022 · 2022
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An Empirical Evaluation of GitHub Copilot’s Code Suggestions. In 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) . 1–5
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Cited alongside, same era.
Grounded Copilot: How Programmers Interact with Code-Generating Models
Shraddha Barke, Michael B. James, and Nadia Polikarpova. 2022 · 2022
Cited alongside, same era.
GitHub Copilot AI pair programmer: Asset or Liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Zhen Ming, and Jiang. 2022 · 2022
Cited alongside, same era.
AI-Driven Development Is Here: Should You Worry?
Neil A Ernst and Gabriele Bavota. 2022 · 2022
Cited alongside, same era.
Nhan Nguyen and Sarah Nadi. 2022 · 2022
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Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
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Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models. In Proceedings of the 18th ACM Conference on International Computing Education Research (Lugano, Switzerland) (ICER 2022) . Association for Computing Machinery, New York, NY, USA
Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen. 2022 · 2022
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Solving Probability and Statistics Problems by Probabilistic Program Synthesis at Human Level and Predicting Solvability. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium , Maria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, and Vania Dimitrova (Eds.). Springer International Publishing, Cham, 612–615
Leonard Tang, Elizabeth Ke, Nikhil Singh, Bo Feng, Derek Austin, Nakul Verma, and Iddo Drori. 2022 · 2022
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Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22) . Association for Computing Machinery, New York, NY, USA, Article 332, 7 pages
Priyan Vaithilingam, Tianyi Zhang, and Elena L. Glassman. 2022 · 2022
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