Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting

03/14/2021
by   Chen Lin, et al.
0

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.

READ FULL TEXT
08/30/2021

CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees

Code summarization aims to generate concise natural language description...
01/19/2022

GAP-Gen: Guided Automatic Python Code Generation

Automatic code generation from natural language descriptions can be high...
12/02/2021

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

Code summarization aims to generate brief natural language descriptions ...
03/18/2022

M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization

Source code summarization aims to generate natural language descriptions...
11/17/2021

GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization

As opposed to natural languages, source code understanding is influenced...
06/01/2021

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

Due to the great potential in facilitating software development, code ge...
04/14/2020

Code Completion using Neural Attention and Byte Pair Encoding

In this paper, we aim to do code completion based on implementing a Neur...