SyGuS-Comp 2018: Results and Analysis

Rajeev AlurDana FismanSaswat PadhiRishabh SinghAbhishek Udupa

The 5 th Annual Syntax-Guided Synthesis Competition, 2018
⟨ SyGuS-Comp 2018 ⟩

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Abstract

Syntax-guided synthesis (SyGuS) is the computational problem of finding an implementation that meets both a semantic constraint given by a logical formula in a background theory , and a syntactic constraint given by a grammar , which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in the SyGuS input format (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 the 5th SyGuS-Comp, five solvers competed on over $1600$ benchmarks across various tracks. This paper presents and analyses the results of this year’s (2018) SyGuS competition.

BibTeX Citation
@article{corr18/alur/syguscomp,
  title         = {SyGuS-Comp 2018: Results and Analysis},
  author        = {Rajeev Alur and
                   Dana Fisman and
                   Saswat Padhi and
                   Rishabh Singh and
                   Abhishek Udupa},
  journal       = {CoRR},
  volume        = {abs/1904.07146},
  year          = {2019},
  eprint        = {1904.07146},
  archivePrefix = {arXiv},
  primaryClass  = {cs.PL},
  url           = {http://arxiv.org/abs/1904.07146}
}