Automatic code summarization frees software developers from the heavy burden
of manual commenting and benefits software development and maintenance.
Abstract Syntax Tree (AST), which depicts the source code's syntactic
structure, has been incorporated to guide the generation of code summaries.
However, existing AST based methods suffer from the difficulty of training and
generate inadequate code summaries. In this paper, we present the Block-wise
Abstract Syntax Tree Splitting method (BASTS for short), which fully utilizes
the rich tree-form syntax structure in ASTs, for improving code summarization.
BASTS splits the code of a method based on the blocks in the dominator tree of
the Control Flow Graph, and generates a split AST for each code split. Each
split AST is then modeled by a Tree-LSTM using a pre-training strategy to
capture local non-linear syntax encoding. The learned syntax encoding is
combined with code encoding, and fed into Transformer to generate high-quality
code summaries. Comprehensive experiments on benchmarks have demonstrated that
BASTS significantly outperforms state-of-the-art approaches in terms of various
evaluation metrics. To facilitate reproducibility, our implementation is
available at this https URL

本文提出了一种基于分块的抽象语法树分裂方法（BASTS），利用 AST 的丰富树形语法结构来改进代码摘要，将其分为块并为每个代码块生成分裂 AST，利用基于树的 LSTM 和 Transformer 生成高质量的代码摘要，实验证明该方法在各种评估指标上显著优于现有方法。