Relational Program Synthesis

TL;DR

This paper introduces relational program synthesis, a new approach for automatically generating multiple programs that satisfy relational specifications, addressing larger search spaces with a novel synthesis algorithm and demonstrating its effectiveness on practical benchmarks.

Contribution

It presents a novel synthesis technique combining CEGIS with relational version space learning, implemented in the Relish framework for various application domains.

Findings

Successfully solves most benchmarks from prior work and online forums.

Significantly outperforms the EUSolver framework in experiments.

Demonstrates practical applicability to string encoders, decoders, and comparators.

Abstract

This paper proposes relational program synthesis, a new problem that concerns synthesizing one or more programs that collectively satisfy a relational specification. As a dual of relational program verification, relational program synthesis is an important problem that has many practical applications, such as automated program inversion and automatic generation of comparators. However, this relational synthesis problem introduces new challenges over its non-relational counterpart due to the combinatorially larger search space. As a first step towards solving this problem, this paper presents a synthesis technique that combines the counterexample-guided inductive synthesis framework with a novel inductive synthesis algorithm that is based on relational version space learning. We have implemented the proposed technique in a framework called Relish, which can be instantiated to different application domains by providing a suitable domain-specific language and the relevant relational specification. We have used the Relish framework to build relational synthesizers to automatically generate string encoders/decoders as well as comparators, and we evaluate our tool on several benchmarks taken from prior work and online forums. Our experimental results show that the proposed technique can solve almost all of these benchmarks and that it significantly outperforms EUSolver, a generic synthesis framework that won the general track of the most recent SyGuS competition.