BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration

TL;DR

BUSTLE introduces a novel program synthesis method that combines bottom-up search with learned guidance to efficiently generate programs from input-output examples, leveraging semantic information and flexible composition.

Contribution

This work presents a new learning-guided bottom-up synthesis approach that improves search efficiency by prioritizing program compositions based on learned models.

Findings

Effective on SyGuS dataset and custom dataset

Outperforms baseline search methods

Leverages semantic execution information

Abstract

Program synthesis is challenging largely because of the difficulty of search in a large space of programs. Human programmers routinely tackle the task of writing complex programs by writing sub-programs and then analyzing their intermediate results to compose them in appropriate ways. Motivated by this intuition, we present a new synthesis approach that leverages learning to guide a bottom-up search over programs. In particular, we train a model to prioritize compositions of intermediate values during search conditioned on a given set of input-output examples. This is a powerful combination because of several emergent properties. First, in bottom-up search, intermediate programs can be executed, providing semantic information to the neural network. Second, given the concrete values from those executions, we can exploit rich features based on recent work on property signatures. Finally, bottom-up search allows the system substantial flexibility in what order to generate the solution, allowing the synthesizer to build up a program from multiple smaller sub-programs. Overall, our empirical evaluation finds that the combination of learning and bottom-up search is remarkably effective, even with simple supervised learning approaches. We demonstrate the effectiveness of our technique on two datasets, one from the SyGuS competition and one of our own creation.