Survey on Neural Network-based Automatic Source Code Summarization Technologies

被引：0
|
作者：
Song X.-T. [1 ]
Sun H.-L. [2 ,3 ,4 ]
机构：
[1] College of Software, Taiyuan University of Technology, Taiyuan
[2] State Key Laboratory of Software Development Environment (Beihang University), Beijing
[3] School of Software, Beihang University, Beijing
[4] Beijing Advanced Innovation Center for Big Data and Brain Computing (Beihang University), Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2022年 / 33卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Deep learning; Intelligent software engineering; Neural network; Program comprehension; Source code summarization;
D O I：
10.13328/j.cnki.jos.006337
中图分类号：
学科分类号：
摘要：
Source code summaries can help software developers comprehend programs faster and better, and assist maintenance developers in accomplishing their tasks efficiently. Since writing summaries by programmers is of high cost and low efficiency, researchers have tried to summarize source code automatically. In recent years, the technologies of neural network-based automatic summarization of source code have become the mainstream techniques of automatic source code summarization, and it is a hot research topic in the domain of intelligent software engineering. Firstly, this paper describes the concept of source code summarization and the definition of automatic source code summarization, presents its development history, and reviews the methods and metrics of the quality evaluation of the generated summaries. Then, it analyzes the general framework and the main challenges of neural network-based automatic code summarization algorithms. In addition, it focuses on the classification of representative algorithms, the design principle, characteristics, and restrictions of each category of algorithms. Finally, it discusses and looks forward to the trends on techniques of neural network-based source code summarization in future. © Copyright 2022, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：55 / 77
页数：22
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