A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
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DOI：
10.18653/v1/p17-2045
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发表时间：
2017-04
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通讯作者：
Pablo Loyola;Edison Marrese-Taylor;Y. Matsuo
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作者：
Pablo Loyola;Edison Marrese-Taylor;Y. Matsuo
我们提出了一个模型来自动描述使用自然语言的程序的源代码中引入的变化。我们的方法接收一组代码提交作为输入，其中包含用户引入的修改和消息。这两种模态用于训练编码器-解码器架构。我们在12个来自四种不同编程语言的真实的开源项目上评估了我们的方法。定量和定性的结果表明，所提出的方法不仅可以在标准的项目内设置，而且在跨项目设置生成可行的和语义上的声音描述。
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.