Scalability and precision improvement of neural program synthesis
Abstract
Mosts of the neural synthesis construct encoder-decoder models to learn a probability distribution over the space of programs. Two drawbacks in such neural program synthesis are that the synthesis scale is relatively small and the correctness of the synthesis result cannot be guaranteed. We address these problems by constructing a framework, which analyzes and solves problems from three dimensions: program space description, model architecture, and result processing. Experiments show that the scalability and precision of synthesis are improved in every dimension.
References
[1]
Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, and Charles A. Sutton. 2018. A Survey of Machine Learning for Big Code and Naturalness. ACM Comput. Surv. 51, 4 (2018), 81:1--81:37.
[2]
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow. 2017. DeepCoder: Learning to Write Programs. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=ByldLrqlx
[3]
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. In Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, Dekai Wu, Marine Carpuat, Xavier Carreras, and Eva Maria Vecchi (Eds.). Association for Computational Linguistics, 103--111.
[4]
Sumit Gulwani, Oleksandr Polozov, and Rishabh Singh. 2017. Program Synthesis. Found. Trends Program. Lang. 4, 1--2 (2017), 1--119.
[5]
Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Fumin Wang, and Andrew W. Senior. 2016. Latent Predictor Networks for Code Generation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7--12, 2016, Berlin, Germany, Volume 1: Long Papers. The Association for Computer Linguistics.
[6]
Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, and Chris Jermaine. 2018. Neural Sketch Learning for Conditional Program Generation. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=HkfXMz-Ab
[7]
Maxim Rabinovich, Mitchell Stern, and Dan Klein. 2017. Abstract Syntax Networks for Code Generation and Semantic Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, 1139--1149.
[8]
Eui Chul Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, and Alex Polozov. 2019. Program Synthesis and Semantic Parsing with Learned Code Idioms. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8--14 December 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, and Roman Garnett (Eds.). 10824--10834. http://papers.nips.cc/paper/9265-program-synthesis-and-semantic-parsing-with-learned-code-idioms
[9]
Pengcheng Yin and Graham Neubig. 2017. A Syntactic Neural Model for General-Purpose Code Generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, 440--450.
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- Scalability and precision improvement of neural program synthesis
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Published In
December 2020
1449 pages
ISBN:9781450367684
DOI:10.1145/3324884
- General Chair:
- John Grundy,
- Program Chairs:
- Claire Le Goues,
- David Lo
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Association for Computing Machinery
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Publication History
Published: 27 January 2021
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- Short-paper
Funding Sources
- National Natural Science Foundation of China
- National key R&D program of China
Conference
ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering
December 21 - 25, 2020
Virtual Event, Australia
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Overall Acceptance Rate 82 of 337 submissions, 24%
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