Ain't Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language

TL;DR

This paper introduces SAPS, a neural network model that translates complex natural language specifications into executable code with high accuracy, eliminating the need for post-processing and leveraging a structure-aware architecture.

Contribution

SAPS is a novel, fully neural, structure-aware program synthesis model that outperforms previous methods on a large dataset without requiring post-processing.

Findings

Achieves over 92% correct program generation.

Performs on par or better than previous state-of-the-art methods.

Does not require post-processing of generated code.

Abstract

Program synthesis from natural language (NL) is practical for humans and, once technically feasible, would significantly facilitate software development and revolutionize end-user programming. We present SAPS, an end-to-end neural network capable of mapping relatively complex, multi-sentence NL specifications to snippets of executable code. The proposed architecture relies exclusively on neural components, and is trained on abstract syntax trees, combined with a pretrained word embedding and a bi-directional multi-layer LSTM for processing of word sequences. The decoder features a doubly-recurrent LSTM, for which we propose novel signal propagation schemes and soft attention mechanism. When applied to a large dataset of problems proposed in a previous study, SAPS performs on par with or better than the method proposed there, producing correct programs in over 92% of cases. In contrast to other methods, it does not require post-processing of the resulting programs, and uses a fixed-dimensional latent representation as the only interface between the NL analyzer and the source code generator.