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dc.contributor.author이우석-
dc.date.accessioned2019-05-22T06:42:55Z-
dc.date.available2019-05-22T06:42:55Z-
dc.date.issued2018-06-
dc.identifier.citationProceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), Page. 436-449en_US
dc.identifier.isbn978-1-4503-5698-5-
dc.identifier.urihttps://dl.acm.org/citation.cfm?doid=3192366.3192410-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/105568-
dc.description.abstractA 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.en_US
dc.language.isoen_USen_US
dc.publisherACMen_US
dc.subjectDomain-specific languagesen_US
dc.subjectStatistical methodsen_US
dc.subjectSynthesisen_US
dc.subjectTransfer learningen_US
dc.titleAccelerating search-based program synthesis using learned probabilistic modelsen_US
dc.typeArticleen_US
dc.identifier.doi10.1145/3192366.3192410-
dc.relation.page436-449-
dc.contributor.googleauthorLee, Woosuk-
dc.contributor.googleauthorHeo, Kihong-
dc.contributor.googleauthorAlur, Rajeev-
dc.contributor.googleauthorNaik, Mayur-
dc.relation.code20180006-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentDIVISION OF COMPUTER SCIENCE-
dc.identifier.pidwoosuk-
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COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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