Accelerating search-based program synthesis using learned probabilistic models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Woosuk | - |
dc.contributor.author | Heo, Kihong | - |
dc.contributor.author | Alur, Rajeev | - |
dc.contributor.author | Naik, Mayur hiru | - |
dc.date.accessioned | 2021-06-22T13:01:53Z | - |
dc.date.available | 2021-06-22T13:01:53Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2018-04 | - |
dc.identifier.issn | 1523-2867 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/7877 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Accelerating search-based program synthesis using learned probabilistic models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Woosuk | - |
dc.identifier.doi | 10.1145/3192366.3192410 | - |
dc.identifier.scopusid | 2-s2.0-85084428299 | - |
dc.identifier.wosid | 000452469600030 | - |
dc.identifier.bibliographicCitation | ACM SIGPLAN Notices, v.53, no.4, pp.436 - 449 | - |
dc.relation.isPartOf | ACM SIGPLAN Notices | - |
dc.citation.title | ACM SIGPLAN Notices | - |
dc.citation.volume | 53 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 436 | - |
dc.citation.endPage | 449 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | Computer programming | - |
dc.subject.keywordPlus | Computer science | - |
dc.subject.keywordPlus | Bench-mark problems | - |
dc.subject.keywordPlus | Domain specific | - |
dc.subject.keywordPlus | Higher-order | - |
dc.subject.keywordPlus | Performance Gain | - |
dc.subject.keywordPlus | Probabilistic modeling | - |
dc.subject.keywordPlus | Probabilistic models | - |
dc.subject.keywordPlus | Program synthesis | - |
dc.subject.keywordPlus | Search Algorithms | - |
dc.subject.keywordPlus | Transfer learning | - |
dc.subject.keywordAuthor | Domain-specific languages | - |
dc.subject.keywordAuthor | Statistical methods | - |
dc.subject.keywordAuthor | Synthesis | - |
dc.subject.keywordAuthor | Transfer learning | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3192366.3192410 | - |
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