A Multi-Encoder Model for Automatic Code Comment Generation

doi:10.21203/rs.3.rs-3218867/v1

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Research Article

A Multi-Encoder Model for Automatic Code Comment Generation

https://doi.org/10.21203/rs.3.rs-3218867/v1

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Automatic code comment generation is an important research topic in software engineering, which aims to help developers understand the source code. However, this task is challenging due to the issues of long dependencies, source code structure information, and out-of-vocabulary (OOV) words. In this paper, we propose HCCM, a novel neural network model that uses three encoders to generate natural language comments for Java methods. The proposed model incorporates three novel techniques: (1) the S-SBT method to encode the abstract syntax tree of the source code; (2) a pointer generation network to copy OOV words from the source code; and (3) a convolutional neural network to capture local features of the source code tokens. We evaluate our model on a state-of-the-art large-scale Java dataset and show that it outperforms the existing methods on several metrics such as BLEU, METEOR, and ROUGE.

multi-encoder model

code comment generation

program understanding

neural network

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This study does not involve any human or animal participants. The authors declare that they have no conflict of interest. The authors adhere to the ethical standards and responsibilities of the journal.

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The authors have no relevant financial or non-financial interests to disclose.

The authors have no competing interests to declare that are relevant to the content of this article.

The authors have no financial or proprietary interests in any material discussed in this article.

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The authors did not receive support from any organization for the submitted work.

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Reviewers invited by journal
16 Sep, 2023
Editor assigned by journal
10 Aug, 2023
First submitted to journal
09 Aug, 2023

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A Multi-Encoder Model for Automatic Code Comment Generation

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