Bottom-up Synthesis of Recursive Functional Programs using Angelic Execution

TL;DR

This paper introduces a bottom-up method for synthesizing recursive functional programs using angelic semantics, enabling effective synthesis from logical specifications and outperforming prior approaches.

Contribution

It presents the first purely bottom-up recursive program synthesis technique utilizing angelic semantics and version space learning.

Findings

Successfully synthesized 94% of benchmarks

Outperformed prior synthesis methods

Demonstrated effectiveness of angelic semantics in synthesis

Abstract

We present a novel bottom-up method for the synthesis of functional recursive programs. While bottom-up synthesis techniques can work better than top-down methods in certain settings, there is no prior technique for synthesizing recursive programs from logical specifications in a purely bottom-up fashion. The main challenge is that effective bottom-up methods need to execute sub-expressions of the code being synthesized, but it is impossible to execute a recursive subexpression of a program that has not been fully constructed yet. In this paper, we address this challenge using the concept of angelic semantics. Specifically, our method finds a program that satisfies the specification under angelic semantics (we refer to this as angelic synthesis), analyzes the assumptions made during its angelic execution, uses this analysis to strengthen the specification, and finally reattempts synthesis with the strengthened specification. Our proposed angelic synthesis algorithm is based on version space learning and therefore deals effectively with many incremental synthesis calls made during the overall algorithm. We have implemented this approach in a prototype called Burst and evaluate it on synthesis problems from prior work. Our experiments show that Burst is able to synthesize a solution to 94% of the benchmarks in our benchmark suite, outperforming prior work.