Existing studies show that code summaries help developers understand and
maintain source code. Unfortunately, these summaries are often missing or
outdated in software projects. Code summarization aims to generate natural
language descriptions automatically for source code. Code summaries are highly
structured and have repetitive patterns. Besides the patternized words, a code
summary also contains important keywords, which are the key to reflecting the
functionality of the code. However, the state-of-the-art approaches perform
poorly on predicting the keywords, which leads to the generated summaries
suffering a loss in informativeness. To alleviate this problem, this paper
proposes a novel retrieve-and-edit approach named EditSum for code
summarization. Specifically, EditSum first retrieves a similar code snippet
from a pre-defined corpus and treats its summary as a prototype summary to
learn the pattern. Then, EditSum edits the prototype automatically to combine
the pattern in the prototype with the semantic information of input code. Our
motivation is that the retrieved prototype provides a good start-point for
post-generation because the summaries of similar code snippets often have the
same pattern. The post-editing process further reuses the patternized words in
the prototype and generates keywords based on the semantic information of input
code. We conduct experiments on a large-scale Java corpus and experimental
results demonstrate that EditSum outperforms the state-of-the-art approaches by
a substantial margin. The human evaluation also proves the summaries generated
by EditSum are more informative and useful. We also verify that EditSum
performs well on predicting the patternized words and keywords.

现有研究表明，代码摘要有助于开发人员理解和维护源代码。然而，软件项目中的这些摘要通常缺失或过时。本文提出了一种名为 EditSum 的新型检索编辑方法，用于代码摘要，旨在自动生成源代码的自然语言描述。实验结果表明，EditSum 在预测模式化单词和关键词方面表现出色，而且人工评估也证明了 EditSum 生成的摘要更具信息量和实用性。