Code comment generation which aims to automatically generate natural language
descriptions for source code, is a crucial task in the field of automatic
software development. Traditional comment generation methods use
manually-crafted templates or information retrieval (IR) techniques to generate
summaries for source code. In recent years, neural network-based methods which
leveraged acclaimed encoder-decoder deep learning framework to learn comment
generation patterns from a large-scale parallel code corpus, have achieved
impressive results. However, these emerging methods only take code-related
information as input. Software reuse is common in the process of software
development, meaning that comments of similar code snippets are helpful for
comment generation. Inspired by the IR-based and template-based approaches, in
this paper, we propose a neural comment generation approach where we use the
existing comments of similar code snippets as exemplars to guide comment
generation. Specifically, given a piece of code, we first use an IR technique
to retrieve a similar code snippet and treat its comment as an exemplar. Then
we design a novel seq2seq neural network that takes the given code, its AST,
its similar code, and its exemplar as input, and leverages the information from
the exemplar to assist in the target comment generation based on the semantic
similarity between the source code and the similar code. We evaluate our
approach on a large-scale Java corpus, which contains about 2M samples, and
experimental results demonstrate that our model outperforms the
state-of-the-art methods by a substantial margin.

本文提出了一种基于 IR 技术和模板的 API 注释生成方法。该方法将机器翻译模型迁移到注释生成任务中并且通过引入仅对模板执行的代码生成步骤，使模型更加准确。在 Java 程序员中取得了 state-of-the-art 的回归结果。