Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting
This work addresses the challenge of generating high-quality code summaries for software developers, offering a novel method that improves upon existing techniques, though it appears incremental as it builds on AST and Transformer-based approaches.
The paper tackles the problem of inadequate code summarization by existing AST-based methods by introducing BASTS, which splits code based on dominator tree blocks and uses Tree-LSTM with pre-training to capture syntax encoding, resulting in significant outperformance over state-of-the-art approaches on benchmarks.
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 https://github.com/XMUDM/BASTS.