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        <title>A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes</title>
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        <namePart type="given">Pablo</namePart>
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    <name type="personal">
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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Min-Yen</namePart>
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    <abstract>We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.</abstract>
    <identifier type="citekey">loyola-etal-2017-neural</identifier>
    <identifier type="doi">10.18653/v1/P17-2045</identifier>
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%0 Conference Proceedings
%T A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
%A Loyola, Pablo
%A Marrese-Taylor, Edison
%A Matsuo, Yutaka
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F loyola-etal-2017-neural
%X We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.
%R 10.18653/v1/P17-2045
%U https://aclanthology.org/P17-2045
%U https://doi.org/10.18653/v1/P17-2045
%P 287-292

Markdown (Informal)

[A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes](https://aclanthology.org/P17-2045) (Loyola et al., ACL 2017)

• A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes (Loyola et al., ACL 2017)

ACL

• Pablo Loyola, Edison Marrese-Taylor, and Yutaka Matsuo. 2017. A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 287–292, Vancouver, Canada. Association for Computational Linguistics.