# SyGuS-Comp 2018: Results and Analysis

## TL;DR

The paper reports on the 2018 Syntax-Guided Synthesis competition, analyzing the performance of various solvers on over 1600 benchmarks to advance research in efficient synthesis techniques.

## Contribution

It provides a comprehensive analysis of solver performances in the 2018 SyGuS-Comp, highlighting progress and challenges in syntax-guided synthesis research.

## Key findings

- Multiple solvers demonstrated varying success rates across benchmarks.
- The competition identified key areas for improvement in synthesis algorithms.
- Benchmark diversity helped evaluate solver robustness.

## 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 $\mathbb{T}$, 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 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.