Exploring Topic Models in Software Engineering Data Analysis: A Survey

被引：0
|
作者：
Sun, Xiaobing [1 ]
Liu, Xiangyue [1 ]
Li, Bin [1 ]
Duan, Yucong [2 ]
Yang, Hui [1 ]
Hu, Jiajun [1 ]
机构：
[1] Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China
[2] Hainan Univ, Haikou, Peoples R China
来源：
2016 17TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD) | 2016年
关键词：
Software engineering; topic models; survey; INFORMATION-RETRIEVAL; FEATURE LOCATION;
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：
Topic models are shown to be effective to mine unstructured software engineering (SE) data. In this paper, we give a simple survey of exploring topic models to support various SE tasks between 2003 and 2015. The survey results show that there is an increasing concern in this area. Among the SE tasks, source code comprehension and software history comprehension are the mostly studied, followed by software defects prediction. However, there is still only a few studies on other SE tasks, such as feature location and regression testing.
引用
收藏
页码：357 / 362
页数：6
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