English

SyGuS-Comp 2017: Results and Analysis

Software Engineering 2017-12-01 v1 Machine Learning Programming Languages

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.

Keywords

Cite

@article{arxiv.1711.11438,
  title  = {SyGuS-Comp 2017: Results and Analysis},
  author = {Rajeev Alur and Dana Fisman and Rishabh Singh and Armando Solar-Lezama},
  journal= {arXiv preprint arXiv:1711.11438},
  year   = {2017}
}

Comments

In Proceedings SYNT 2017, arXiv:1711.10224. arXiv admin note: text overlap with arXiv:1611.07627, arXiv:1602.01170

R2 v1 2026-06-22T23:02:29.855Z