The 5 ^{th} Annual Syntax-Guided Synthesis Competition, 2018

⟨ SyGuS-Comp 2018 ⟩

*Syntax-guided synthesis (SyGuS)* is the computational problem of finding an implementation (f)
that meets both a semantic constraint given by a logical formula (\varphi) 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 5^{th} 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.

```
@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}
}
```