dc.contributor.author | Alur, Rajeev | |
dc.contributor.author | Fisman, Dana | |
dc.contributor.author | Singh, Rishabh | |
dc.contributor.author | Solar-Lezama, Armando | |
dc.date.accessioned | 2021-11-09T15:05:18Z | |
dc.date.available | 2021-11-09T15:05:18Z | |
dc.date.issued | 2016-11-22 | |
dc.identifier.issn | 2075-2180 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/137904 | |
dc.description.abstract | © R. Alur, D. Fisman, R. Singh & A. Solar-Lezama. Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula φ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in SyGuS-IF, a language that is built on top of SMT-LIB. The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In this year's competition we added a new track devoted to programming by examples. This track consisted of two categories, one using the theory of bit-vectors and one using the theory of strings. This paper presents and analyses the results of SyGuS-Comp'16. | en_US |
dc.language.iso | en | |
dc.publisher | Open Publishing Association | en_US |
dc.relation.isversionof | 10.4204/eptcs.229.13 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | arXiv | en_US |
dc.title | SyGuS-Comp 2016: Results and Analysis | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Alur, Rajeev, Fisman, Dana, Singh, Rishabh and Solar-Lezama, Armando. 2016. "SyGuS-Comp 2016: Results and Analysis." 229. | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2019-07-10T13:04:40Z | |
dspace.date.submission | 2019-07-10T13:04:41Z | |
mit.journal.volume | 229 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |