Accelerating search-based program synthesis using learned probabilistic models
- Authors
- Lee, Woosuk; Heo, Kihong; Alur, Rajeev; Naik, Mayur hiru
- Issue Date
- Apr-2018
- Publisher
- Association for Computing Machinery
- Keywords
- Domain-specific languages; Statistical methods; Synthesis; Transfer learning
- Citation
- ACM SIGPLAN Notices, v.53, no.4, pp.436 - 449
- Indexed
- SCIE
SCOPUS
- Journal Title
- ACM SIGPLAN Notices
- Volume
- 53
- Number
- 4
- Start Page
- 436
- End Page
- 449
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/7877
- DOI
- 10.1145/3192366.3192410
- ISSN
- 1523-2867
- Abstract
- A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the grammar of possible programs with a probabilistic model dictating the likelihood of each program. We develop a weighted search algorithm to efficiently enumerate programs in order of their likelihood. We also propose a method based on transfer learning that enables to effectively learn a powerful model, called probabilistic higher-order grammar, from known solutions in a domain. We have implemented our approach in a tool called Euphony and evaluate it on SyGuS benchmark problems from a variety of domains. We show that Euphony can learn good models using easily obtainable solutions, and achieves significant performance gains over existing general-purpose as well as domain-specific synthesizers. © 2018 ACM.
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