Source code summarization aims to generate natural language descriptions of
code snippets. Many existing studies learn the syntactic and semantic knowledge
of code snippets from their token sequences and Abstract Syntax Trees (ASTs).
They use the learned code representations as input to code summarization
models, which can accordingly generate summaries describing source code.
Traditional models traverse ASTs as sequences or split ASTs into paths as
input. However, the former loses the structural properties of ASTs, and the
latter destroys the overall structure of ASTs. Therefore, comprehensively
capturing the structural features of ASTs in learning code representations for
source code summarization remains a challenging problem to be solved. In this
paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer
for source code Summarization. M2TS uses a multi-scale AST feature extraction
method, which can extract the structures of ASTs more completely and accurately
at multiple local and global levels. To complement missing semantic information
in ASTs, we also obtain code token features, and further combine them with the
extracted AST features using a cross modality fusion method that not only fuses
the syntactic and contextual semantic information of source code, but also
highlights the key features of each modality. We conduct experiments on two
Java and one Python datasets, and the experimental results demonstrate that
M2TS outperforms current state-of-the-art methods. We release our code at
this https URL.

本研究旨在提出一种基于多尺度、多模态方法和 Transformer 的源代码摘要生成模型 (M2TS)，该模型使用多尺度 AST 特征提取方法、代码词法特征和跨模态融合方法，以更全面准确地抓取 AST 的结构特征，同时融合语法和上下文语义信息，从而生成源代码的高质量摘要并在实验证明了其优于当前最新研究。