Code comment generation is the task of generating a high-level natural
language description for a given code method or function. Although researchers
have been studying multiple ways to generate code comments automatically,
previous work mainly considers representing a code token in its entirety
semantics form only (e.g., a language model is used to learn the semantics of a
code token), and additional code properties such as the tree structure of a
code are included as an auxiliary input to the model. There are two
limitations: 1) Learning the code token in its entirety form may not be able to
capture information succinctly in source code, and 2) The code token does not
contain additional syntactic information, inherently important in programming
languages.
In this paper, we present LAnguage Model and Named Entity Recognition
(LAMNER), a code comment generator capable of encoding code constructs
effectively and capturing the structural property of a code token. A
character-level language model is used to learn the semantic representation to
encode a code token. For the structural property of a token, a Named Entity
Recognition model is trained to learn the different types of code tokens. These
representations are then fed into an encoder-decoder architecture to generate
code comments. We evaluate the generated comments from LAMNER and other
baselines on a popular Java dataset with four commonly used metrics. Our
results show that LAMNER is effective and improves over the best baseline model
in BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-L, METEOR, and CIDEr by 14.34%,
18.98%, 21.55%, 23.00%, 10.52%, 1.44%, and 25.86%, respectively. Additionally,
we fused LAMNER's code representation with the baseline models, and the fused
models consistently showed improvement over the non-fused models. The human
evaluation further shows that LAMNER produces high-quality code comments.

本文提出 LAnguage Model and Named Entity Recognition (LAMNER) 模型，通过字符级别语言模型来学习代码单元的语义表示，并使用命名实体识别模型来学习不同类型的代码标记的结构属性，在编码器 - 解码器结构中生成代码注释。在流行的 Java 数据集上进行评估，结果表明 LAMNER 可以有效地生成注释，并在 BLEU-1、BLEU-2、BLEU-3、BLEU-4、ROUGE-L、METEOR 和 CIDEr 等四种常用度量标准上改进了最佳基准模型的 14.34％，18.98％，21.55％，23.00％，10.52％，1.44％和 25.86％。同时，融合 LAMNER 的代码表示和基准模型，融合模型显示出优于非融合模型的结果。人工评估进一步表明，LAMNER 可以生成高质量的代码注释。

利用字符语言模型和命名实体识别生成代码注释

LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition

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。

检索和优化：基于示例的神经评论生成

Retrieve and Refine: Exemplar-based Neural Comment Generation

The relationship of comments to code, and in particular, the task of
generating useful comments given the code, has long been of interest. The
earliest approaches have been based on strong syntactic theories of
comment-structures, and relied on textual templates. More recently, researchers
have applied deep learning methods to this task, and specifically, trainable
generative translation models which are known to work very well for Natural
Language translation (e.g., from German to English). We carefully examine the
underlying assumption here: that the task of generating comments sufficiently
resembles the task of translating between natural languages, and so similar
models and evaluation metrics could be used. We analyze several recent
code-comment datasets for this task: CodeNN, DeepCom, FunCom, and DocString. We
compare them with WMT19, a standard dataset frequently used to train state of
the art natural language translators. We found some interesting differences
between the code-comment data and the WMT19 natural language data. Next, we
describe and conduct some studies to calibrate BLEU (which is commonly used as
a measure of comment quality). using "affinity pairs" of methods, from
different projects, in the same project, in the same class, etc; Our study
suggests that the current performance on some datasets might need to be
improved substantially. We also argue that fairly naive information retrieval
(IR) methods do well enough at this task to be considered a reasonable
baseline. Finally, we make some suggestions on how our findings might be used
in future research in this area.

本文在研究代码注释生成中，分析了采用翻译模型的可行性以及 BLEU 得分的校准方法，并提出了基于信息检索方法的合理基准线以及未来研究方向的建议。

代码到注释的翻译：数据、度量、基准和评估

Code to Comment "Translation": Data, Metrics, Baselining & Evaluation

Existing leading code comment generation approaches with the
structure-to-sequence framework ignores the type information of the
interpretation of the code, e.g., operator, string, etc. However, introducing
the type information into the existing framework is non-trivial due to the
hierarchical dependence among the type information. In order to address the
issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for
the code comment generation task which considers the source code as an N-ary
tree with type information associated with each node. Specifically, our
framework is featured with a Type-associated Encoder and a Type-restricted
Decoder which enables adaptive summarization of the source code. We further
propose a hierarchical reinforcement learning method to resolve the training
difficulties of our proposed framework. Extensive evaluations demonstrate the
state-of-the-art performance of our framework with both the auto-evaluated
metrics and case studies.

提出了一种考虑代码类型信息的编码器 - 解码器框架，解决现有结构到序列框架在生成代码注释时忽略类型信息的问题，使用了基于层次强化学习的方法解决训练困难，并在自动评估指标和案例研究上达到了最先进的性能水平。