A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

被引：61
作者：
Loyola, Pablo [1 ]
Marrese-Taylor, Edison [1 ]
Matsuo, Yutaka [1 ]
机构：
[1] Univ Tokyo, Grad Sch Engn, Tokyo, Japan
来源：
PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2 | 2017年
关键词：
D O I：
10.18653/v1/P17-2045
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：287 / 292
页数：6
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