Source Code Summarization with Structural Relative Position Guided Transformer

TL;DR

This paper introduces SCRIPT, a Transformer-based model that incorporates structural relative positions from Abstract Syntax Trees to improve source code summarization, outperforming existing methods on standard benchmarks.

Contribution

The paper proposes a novel Structural Relative Position guided Transformer (SCRIPT) that models structural dependencies in source code using AST-based relative positions, enhancing code semantic understanding.

Findings

SCRIPT outperforms state-of-the-art methods by at least 1.6% in BLEU score.

SCRIPT achieves 1.4% higher ROUGE-L score than previous approaches.

SCRIPT improves METEOR score by 2.8% on benchmark datasets.

Abstract

Source code summarization aims at generating concise and clear natural language descriptions for programming languages. Well-written code summaries are beneficial for programmers to participate in the software development and maintenance process. To learn the semantic representations of source code, recent efforts focus on incorporating the syntax structure of code into neural networks such as Transformer. Such Transformer-based approaches can better capture the long-range dependencies than other neural networks including Recurrent Neural Networks (RNNs), however, most of them do not consider the structural relative correlations between tokens, e.g., relative positions in Abstract Syntax Trees (ASTs), which is beneficial for code semantics learning. To model the structural dependency, we propose a Structural Relative Position guided Transformer, named SCRIPT. SCRIPT first obtains the structural relative positions between tokens via parsing the ASTs of source code, and then passes them into two types of Transformer encoders. One Transformer directly adjusts the input according to the structural relative distance; and the other Transformer encodes the structural relative positions during computing the self-attention scores. Finally, we stack these two types of Transformer encoders to learn representations of source code. Experimental results show that the proposed SCRIPT outperforms the state-of-the-art methods by at least 1.6%, 1.4% and 2.8% with respect to BLEU, ROUGE-L and METEOR on benchmark datasets, respectively. We further show that how the proposed SCRIPT captures the structural relative dependencies.