Code Representation Learning with Pr\"ufer Sequences

TL;DR

This paper introduces a novel code representation using Prüfer sequences of Abstract Syntax Trees, enabling more effective deep learning models for code summarization by capturing structural information efficiently.

Contribution

The paper proposes a lossless, concise AST encoding method with Prüfer sequences, improving deep learning performance in code understanding tasks.

Findings

Outperforms recent deep-learning models in code summarization tasks

Provides a lossless and concise structural code representation

Enhances the exploitation of syntactic roles in code modeling

Abstract

An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for tasks in computer program comprehension, such as automated code summarization and documentation. A significant challenge is to find a sequential representation that captures the structural/syntactic information in a computer program and facilitates the training of the learning models. In this paper, we propose to use the Pr\"ufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Empirical studies on real-world benchmark datasets, using a sequence-to-sequence learning model we designed for code summarization, show that our Pr\"ufer-sequence-based representation is indeed highly effective and efficient, outperforming significantly all the recently-proposed deep-learning models we used as the baseline models.