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Automatic code summarization frees software developers from the heavy burdenof manual commenting and benefits software development and maintenance.Abstract Syntax Tree (AST), which depicts the source code's syntacticstructure, has been incorporated to guide the generation of code summaries.However, existing AST based methods suffer from the difficulty of training andgenerate inadequate code summaries. In this paper, we present the Block-wiseAbstract Syntax Tree Splitting method (BASTS for short), which fully utilizesthe 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 ofthe Control Flow Graph, and generates a split AST for each code split. Eachsplit AST is then modeled by a Tree-LSTM using a pre-training strategy tocapture local non-linear syntax encoding. The learned syntax encoding iscombined with code encoding, and fed into Transformer to generate high-qualitycode summaries. Comprehensive experiments on benchmarks have demonstrated thatBASTS significantly outperforms state-of-the-art approaches in terms of variousevaluation metrics. To facilitate reproducibility, our implementation isavailable at https://github.com/XMUDM/BASTS.

Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting