Article

A machine learning framework for programming by example

Authors:

Aditya Krishna Menon,

Butler Lampson,

Adam Tauman KalaiAuthors Info & Claims

ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28

Pages I-187 - I-195

Published: 16 June 2013 Publication History

Abstract

Learning programs is a timely and interesting challenge. In Programming by Example (PBE), a system attempts to infer a program from input and output examples alone, by searching for a composition of some set of base functions. We show how machine learning can be used to speed up this seemingly hopeless search problem, by learning weights that relate textual features describing the provided input-output examples to plausible sub-components of a program. This generic learning framework lets us address problems beyond the scope of earlier PBE systems. Experiments on a prototype implementation show that learning improves search and ranking on a variety of text processing tasks found on help forums.

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Ji RXia JXiong YHu Z(2021)Generalizable synthesis through unificationProceedings of the ACM on Programming Languages10.1145/34855445:OOPSLA(1-28)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485544
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cover image Guide Proceedings

ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28

June 2013

2534 pages

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JMLR.org

Publication History

Published: 16 June 2013

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Cited By

Takenouchi KIshio TOkada JSakata Y(2021)PATSQLProceedings of the VLDB Endowment10.14778/3476249.347625314:11(1937-1949)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.14778/3476249.3476253
Ji RXia JXiong YHu Z(2021)Generalizable synthesis through unificationProceedings of the ACM on Programming Languages10.1145/34855445:OOPSLA(1-28)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485544
Huang JSmith CBastani OSingh RAlbarghouthi ANaik MDaumé HSingh A(2020)Generating programmatic referring expressions via program synthesisProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525356(4495-4506)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525356
Chen YMartins RFeng YDumas MPfahl DApel SRusso A(2019)Maximal multi-layer specification synthesisProceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3338906.3338951(602-612)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3338906.3338951
Shi KSteinhardt JLiang P(2019)FrAngel: component-based synthesis with control structuresProceedings of the ACM on Programming Languages10.1145/32903863:POPL(1-29)Online publication date: 2-Jan-2019
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Ellis KMorales LSablé-Meyer MSolar-Lezama ATenenbaum J(2018)Library learning for neurally-guided Bayesian program inductionProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327757.3327878(7816-7826)Online publication date: 3-Dec-2018
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Lee WHeo KAlur RNaik M(2018)Accelerating search-based program synthesis using learned probabilistic modelsACM SIGPLAN Notices10.1145/3296979.319241053:4(436-449)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3296979.3192410
Feng YMartins RBastani ODillig I(2018)Program synthesis using conflict-driven learningACM SIGPLAN Notices10.1145/3296979.319238253:4(420-435)Online publication date: 11-Jun-2018
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Bielik PFischer MVechev M(2018)Robust relational layout synthesis from examples for AndroidProceedings of the ACM on Programming Languages10.1145/32765262:OOPSLA(1-29)Online publication date: 24-Oct-2018
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