Code comments are significantly helpful in comprehending software programs
and also aid developers to save a great deal of time in software maintenance.
Code comment generation aims to automatically predict comments in natural
language given a code snippet. Several works investigate the effect of
integrating external knowledge on the quality of generated comments. In this
study, we propose a solution, namely APIContext2Com, to improve the
effectiveness of generated comments by incorporating the pre-defined
Application Programming Interface (API) context. The API context includes the
definition and description of the pre-defined APIs that are used within the
code snippets. As the detailed API information expresses the functionality of a
code snippet, it can be helpful in better generating the code summary. We
introduce a seq-2-seq encoder-decoder neural network model with different sets
of multiple encoders to effectively transform distinct inputs into target
comments. A ranking mechanism is also developed to exclude non-informative
APIs, so that we can filter out unrelated APIs. We evaluate our approach using
the Java dataset from CodeSearchNet. The findings reveal that the proposed
model improves the best baseline by 1.88 (8.24 %), 2.16 (17.58 %), 1.38 (18.3
%), 0.73 (14.17 %), 1.58 (14.98 %) and 1.9 (6.92 %) for BLEU1, BLEU2, BLEU3,
BLEU4, METEOR, ROUGE-L respectively. Human evaluation and ablation studies
confirm the quality of the generated comments and the effect of architecture
and ranking APIs.

本文提出 APIContext2Com，利用预定义的 API 上下文来改善自动生成的代码注释的效果，介绍一种序列到序列编码器 - 解码器神经网络模型，它能够有效地将不同的输入转换为目标注释，并开发了一种排名机制来排除无关的 API。作者在 CodeSearchNet 数据集上评估了该方法，发现在 BLEU1、BLEU2、BLEU3、BLEU4、METEOR 和 ROUGE-L 方面分别提高了 1.88（8.24％）、2.16（17.58％）、1.38（18.3％）、0.73（14.17％）、1.58（14.98％）和 1.9（6.92％），同时作者进行人体评估和消融研究确认了该方法的有效性和架构和排名 API 的影响。

APIContext2Com: 将预定义 API 文档纳入考虑的代码注释生成

APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation

Semantic code search is the task of retrieving relevant code snippet given a
natural language query. Different from typical information retrieval tasks,
code search requires to bridge the semantic gap between the programming
language and natural language, for better describing intrinsic concepts and
semantics. Recently, deep neural network for code search has been a hot
research topic. Typical methods for neural code search first represent the code
snippet and query text as separate embeddings, and then use vector distance
(e.g. dot-product or cosine) to calculate the semantic similarity between them.
There exist many different ways for aggregating the variable length of code or
query tokens into a learnable embedding, including bi-encoder, cross-encoder,
and poly-encoder. The goal of the query encoder and code encoder is to produce
embeddings that are close with each other for a related pair of query and the
corresponding desired code snippet, in which the choice and design of encoder
is very significant.
In this paper, we propose a novel deep semantic model which makes use of the
utilities of not only the multi-modal sources, but also feature extractors such
as self-attention, the aggregated vectors, combination of the intermediate
representations. We apply the proposed model to tackle the CodeSearchNet
challenge about semantic code search. We align cross-lingual embedding for
multi-modality learning with large batches and hard example mining, and combine
different learned representations for better enhancing the representation
learning. Our model is trained on CodeSearchNet corpus and evaluated on the
held-out data, the final model achieves 0.384 NDCG and won the first place in
this benchmark. Models and code are available at
this https URL.

本文提出了一种新颖的深度语义模型，利用多模式来源和特征提取器的效用，将交叉语言嵌入与大批量和难样例挖掘结合起来，结合不同的学习表示来增强表示学习，以解决语义代码搜索任务，该模型在 CodeSearchNet 语料库上训练并在保留数据上评估，取得了 0.384 NDCG 的优异成绩，并在该基准测试中获得第一名。