Associating Natural Language Comment and Source Code Entities

被引:0
|
作者
Panthaplackel, Sheena [1 ]
Gligoric, Milos [2 ]
Mooney, Raymond J. [1 ]
Li, Junyi Jessy [3 ]
机构
[1] Univ Texas Austin, Dept Comp Sci, Austin, TX 78712 USA
[2] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA
[3] Univ Texas Austin, Dept Linguist, Austin, TX 78712 USA
来源
THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2020年 / 34卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.
引用
收藏
页码:8592 / 8599
页数:8
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