AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization

TL;DR

AST-Transformer is a novel method that efficiently encodes Abstract Syntax Trees for code summarization, significantly reducing computational complexity while improving performance over existing approaches.

Contribution

The paper introduces AST-Transformer, a new model that effectively encodes ASTs for code summarization, addressing the challenge of large AST sizes and computational costs.

Findings

Outperforms state-of-the-art methods in code summarization

Reduces encoding computational complexity by 90-95%

Achieves substantial performance improvements

Abstract

Code summarization aims to generate brief natural language descriptions for source code. As source code is highly structured and follows strict programming language grammars, its Abstract Syntax Tree (AST) is often leveraged to inform the encoder about the structural information. However, ASTs are usually much longer than the source code. Current approaches ignore the size limit and simply feed the whole linearized AST into the encoder. To address this problem, we propose AST-Transformer to efficiently encode tree-structured ASTs. Experiments show that AST-Transformer outperforms the state-of-arts by a substantial margin while being able to reduce $90\sim95\%$ of the computational complexity in the encoding process.