SyGuS-comp 2017: Results and analysis

Rajeev Alur; Dana Fisman; Rishabh Singh; Armando Solar-Lezama

doi:10.4204/EPTCS.260.9

SyGuS-comp 2017: Results and analysis

Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama

Department of Computer Science

Research output: Contribution to journal › Conference article › peer-review

15 Scopus citations

Abstract

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula j 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 six new solvers competed on over 1500 benchmarks. This paper presents and analyses the results of SyGuS-Comp'17.

Original language	English
Pages (from-to)	97-115
Number of pages	19
Journal	Electronic Proceedings in Theoretical Computer Science, EPTCS
Volume	260
DOIs	https://doi.org/10.4204/EPTCS.260.9
State	Published - 28 Nov 2017
Event	6th Workshop on Synthesis, SYNT 2017 - Heidelberg, Germany Duration: 22 Jul 2017 → …

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Software

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10.4204/EPTCS.260.9

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SyGuS-comp 2017: Results and analysis

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