# SyGuS-Comp 2017: Results and Analysis

arXiv: https://arxiv.org/abs/1711.11438 · HTML: https://mlsanity.com/paper/1711.11438/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation](https://mlsanity.com/paper/2310.00796/) (2023)
- [Bayesian Program Learning by Decompiling Amortized Knowledge](https://mlsanity.com/paper/2306.07856/) (2023)

## Abstract (arXiv)

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.