代码注释自动生成方法综述

在软件的开发和维护过程中，与代码对应的注释经常存在缺失、不足或者与代码实际内容不匹配等问题，但手工编写代码注释对开发人员来说费时费力，且注释质量难以保证，因此亟需研究人员提出有效的代码注释自动生成方法.代码注释自动生成问题是当前程序理解研究领域的一个研究热点，论文对该问题进行了系统综述.主要将已有的自动生成方法细分为三类：基于模板的方法、基于信息检索的方法和基于深度学习的方法.论文依次对每一类方法的已有研究成果进行了系统的梳理、总结和点评.随后分析了已有的实证研究中经常使用的语料库和主要的注释质量评估方法，以利于针对该问题的后续研究可以进行合理的实验设计.最后总结全文，并对未来值得关注的研究方向进行了展望.

Survey of State-of-the-art Automatic Code Comment Generation

During software development and maintenance, code comments often have some problems, such as missing, insufficient, or mismatching with code content. Writing high-quality code comments takes time and effort for developers, and the quality can not be guaranteed, so it is urgent for researchers to design effective automatic code comment generation methods. The automatic code comment generation issue is an active research topic in the program comprehension domain. In this paper, we conduct a systematic review of this research topic. The existing methods are divided into three categories:template-based generation methods, information retrieval-based methods, and deep learning-based methods. We analyze and summarize related studies for each category. Then we analyze the corpora and comment quality evaluation methods that are often used in previous studies, which can facilitate the experimental study for future studies. Finally, we summarize this paper and discuss the potential research direction in the future.