BIROn - Birkbeck Institutional Research Online

    Automatic source code summarization with graph attention networks

    Yu, Z. and Shen, J. and Zhang, X. and Yang, W. and Han, Tingting and Chen, Taolue (2022) Automatic source code summarization with graph attention networks. Journal of Systems and Software 188 , ISSN 0164-1212.

    [img]
    Preview
    Text
    48300a.pdf - Published Version of Record
    Available under License Creative Commons Attribution.

    Download (2MB) | Preview

    Abstract

    Source code summarization aims to generate concise descriptions for code snippets in a natural language, thereby facilitates program comprehension and software maintenance. In this paper, we propose a novel approach–GSCS–to automatically generate summaries for Java methods, which leverages both semantic and structural information of the code snippets. To this end, GSCS utilizes Graph Attention Networks to process the tokenized abstract syntax tree of the program, which employ a multi-head attention mechanism to learn node features in diverse representation sub-spaces, and aggregate features by assigning different weights to its neighbor nodes. GSCS further harnesses an additional RNN-based sequence model to obtain the semantic features and optimizes the structure by combining its output with a transformed embedding layer. We evaluate our approach on two widely-adopted Java datasets; the experiment results confirm that GSCS outperforms the state-of-the-art baselines.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Tingting Han
    Date Deposited: 30 May 2022 14:47
    Last Modified: 30 May 2022 20:05
    URI: https://eprints.bbk.ac.uk/id/eprint/48300

    Statistics

    Activity Overview
    6 month trend
    99Downloads
    6 month trend
    43Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item