Action Word Prediction for Neural Source Code Summarization
Action Word Prediction for Neural Source Code Summarization
复制标题
DOI：
10.1109/saner50967.2021.00038
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
S. Haque;Aakash Bansal;Lingfei Wu;Collin McMillan
中科院分区：
文献类型：
--
作者：
S. Haque;Aakash Bansal;Lingfei Wu;Collin McMillan
源代码摘要是为源代码创建简短的自然语言描述的任务。代码摘要是许多软件文档(如Java Docs)的基础，其中非常简短的注释(如“添加客户对象”)可帮助程序员快速理解一段代码。近年来，代码自动摘要已经成为一个很有价值的研究目标，基于神经网络的方法得到了快速的发展。然而，正如我们将在本文中所展示的那样，良好摘要的生成依赖于这些摘要中动作词的生成：如果用“删除”替换“添加”，则上述示例的含义将完全改变。在本文中，我们主张特别重视动作词预测，将其作为更好的代码摘要的重要垫脚石问题--当前的技术试图连同整个摘要一起预测动作词，但动作词预测本身是相当困难的。我们展示了代码摘要问题的价值，探索了当前基线的性能，并为未来的研究提供了建议。
Source code summarization is the task of creating short, natural language descriptions of source code. Code summarization is the backbone of much software documentation such as JavaDocs, in which very brief comments such as "adds the customer object" help programmers quickly understand a snippet of code. In recent years, automatic code summarization has become a high value target of research, with approaches based on neural networks making rapid progress. However, as we will show in this paper, the production of good summaries relies on the production of the action word in those summaries: the meaning of the example above would be completely changed if "removes" were substituted for "adds." In this paper, we advocate for a special emphasis on action word prediction as an important stepping stone problem towards better code summarization – current techniques try to predict the action word along with the whole summary, and yet action word prediction on its own is quite difficult. We show the value of the problem for code summaries, explore the performance of current baselines, and provide recommendations for future research.