Program Synthesis and Semantic Parsing with Learned Code Idioms

TL;DR

This paper introduces PATOIS, a neural program synthesis system that leverages learned code idioms to improve accuracy in semantic parsing tasks by combining high-level and low-level reasoning.

Contribution

It presents a novel approach to incorporate automatically mined code idioms into neural synthesis, enhancing program generation capabilities.

Findings

Using learned code idioms improves synthesis accuracy.

PATOIS effectively combines high-level and low-level reasoning.

System outperforms baseline models on semantic parsing datasets.

Abstract

Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present PATOIS, a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step. It accomplishes this by automatically mining common code idioms from a given corpus, incorporating them into the underlying language for neural synthesis, and training a tree-based neural synthesizer to use these idioms during code generation. We evaluate PATOIS on two complex semantic parsing datasets and show that using learned code idioms improves the synthesizer's accuracy.