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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Learning to Generate Comments for API-Based Code Snippets

verfasst von : Yangyang Lu, Zelong Zhao, Ge Li, Zhi Jin

Erschienen in: Software Engineering and Methodology for Emerging Domains

Verlag: Springer Singapore

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Abstract

Comments play an important role in software developments. They can not only improve the readability and maintainability of source code, but also provide significant resource for software reuse. However, it is common that lots of code in software projects lacks of comments. Automatic comment generation is proposed to address this issue. In this paper, we present an end-to-end approach to generate comments for API-based code snippets automatically. It takes API sequences as the core semantic representations of method-level API-based code snippets and generates comments from API sequences with sequence-to-sequence neural models. In our evaluation, we extract 217K pairs of code snippets and comments from Java projects to construct the dataset. Finally, our approach gains 36.48% BLEU-4 score and 9.90% accuracy on the test set. We also do case studies on generated comments, which presents that our approach generates reasonable and effective comments for API-based code snippets.

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Metadaten
Titel
Learning to Generate Comments for API-Based Code Snippets
verfasst von
Yangyang Lu
Zelong Zhao
Ge Li
Zhi Jin
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Software Engineering and Methodology for Emerging Domains

Print ISBN: 978-981-15-0309-2

Electronic ISBN: 978-981-15-0310-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-15-0310-8_1

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