Competition-level code generation with AlphaCode

Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform po...

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Published in:Science (American Association for the Advancement of Science) Vol. 378; no. 6624; p. 1092
Main Authors: Li, Yujia, Choi, David, Chung, Junyoung, Kushman, Nate, Schrittwieser, Julian, Leblond, Rémi, Eccles, Tom, Keeling, James, Gimeno, Felix, Dal Lago, Agustin, Hubert, Thomas, Choy, Peter, de Masson d'Autume, Cyprien, Babuschkin, Igor, Chen, Xinyun, Huang, Po-Sen, Welbl, Johannes, Gowal, Sven, Cherepanov, Alexey, Molloy, James, Mankowitz, Daniel J, Sutherland Robson, Esme, Kohli, Pushmeet, de Freitas, Nando, Kavukcuoglu, Koray, Vinyals, Oriol
Format: Journal Article
Language:English
Published: United States 09.12.2022
ISSN:1095-9203, 1095-9203
Online Access:Get more information
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Summary:Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions.
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ISSN:1095-9203
1095-9203
DOI:10.1126/science.abq1158