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        <title>Learning to Update Natural Language Comments Based on Code Changes</title>
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        <namePart type="given">Sheena</namePart>
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        <namePart type="given">Pengyu</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment. We compare our approach against multiple baselines using both automatic metrics and human evaluation. Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.</abstract>
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%0 Conference Proceedings
%T Learning to Update Natural Language Comments Based on Code Changes
%A Panthaplackel, Sheena
%A Nie, Pengyu
%A Gligoric, Milos
%A Li, Junyi Jessy
%A Mooney, Raymond
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F panthaplackel-etal-2020-learning
%X We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment. We compare our approach against multiple baselines using both automatic metrics and human evaluation. Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.
%R 10.18653/v1/2020.acl-main.168
%U https://aclanthology.org/2020.acl-main.168
%U https://doi.org/10.18653/v1/2020.acl-main.168
%P 1853-1868

Markdown (Informal)

[Learning to Update Natural Language Comments Based on Code Changes](https://aclanthology.org/2020.acl-main.168) (Panthaplackel et al., ACL 2020)

• Learning to Update Natural Language Comments Based on Code Changes (Panthaplackel et al., ACL 2020)

ACL

• Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, and Raymond Mooney. 2020. Learning to Update Natural Language Comments Based on Code Changes. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1853–1868, Online. Association for Computational Linguistics.