Learning Python Code Suggestion with a Sparse Pointer Network

TL;DR

This paper presents a neural language model with a sparse pointer network that significantly improves Python code suggestion accuracy, especially for long-range identifier references, by capturing dependencies over many tokens.

Contribution

The paper introduces a novel sparse pointer network architecture for neural language models, enhancing long-range dependency modeling in code suggestion systems for dynamic languages.

Findings

Achieved a 5% increase in code suggestion accuracy over baseline models.

Reduced perplexity significantly with the new model.

Predicted identifiers with 13 times higher accuracy for distant references.

Abstract

To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While current IDEs work well for statically-typed languages, their reliance on type annotations means that they do not provide the same level of support for dynamic programming languages as for statically-typed languages. Moreover, suggestion engines in modern IDEs do not propose expressions or multi-statement idiomatic code. Recent work has shown that language models can improve code suggestion systems by learning from software repositories. This paper introduces a neural language model with a sparse pointer network aimed at capturing very long-range dependencies. We release a large-scale code suggestion corpus of 41M lines of Python code crawled from GitHub. On this corpus, we found standard neural language models to perform well at suggesting local phenomena, but struggle to refer to identifiers that are introduced many tokens in the past. By augmenting a neural language model with a pointer network specialized in referring to predefined classes of identifiers, we obtain a much lower perplexity and a 5 percentage points increase in accuracy for code suggestion compared to an LSTM baseline. In fact, this increase in code suggestion accuracy is due to a 13 times more accurate prediction of identifiers. Furthermore, a qualitative analysis shows this model indeed captures interesting long-range dependencies, like referring to a class member defined over 60 tokens in the past.