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Link to original content: https://doi.org/10.48550/arXiv.1711.11438
[1711.11438] SyGuS-Comp 2017: Results and Analysis
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Computer Science > Software Engineering

arXiv:1711.11438 (cs)
[Submitted on 29 Nov 2017]

Title:SyGuS-Comp 2017: Results and Analysis

Authors:Rajeev Alur (1), Dana Fisman (2), Rishabh Singh (3), Armando Solar-Lezama (4) ((1) University of Pennsylvania, (2) Ben-Gurion University, (3) Microsoft Research, Redmond, (4) Massachusetts Institute of Technology)
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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 phi 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.
Comments: In Proceedings SYNT 2017, arXiv:1711.10224. arXiv admin note: text overlap with arXiv:1611.07627, arXiv:1602.01170
Subjects: Software Engineering (cs.SE); Machine Learning (cs.LG); Programming Languages (cs.PL)
Cite as: arXiv:1711.11438 [cs.SE]
  (or arXiv:1711.11438v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.1711.11438
Journal reference: EPTCS 260, 2017, pp. 97-115
Related DOI: https://doi.org/10.4204/EPTCS.260.9

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Wed, 29 Nov 2017 01:31:10 UTC (1,404 KB)
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