DR_PSF: Enhancing Developer Recommendation by Leveraging Personalized Source-Code Files

被引:12
|
作者
Yang, Hui [1 ]
Sun, Xiaobing [1 ,3 ]
Li, Bin [1 ,3 ]
Duan, Yucong [2 ]
机构
[1] Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China
[2] Hainan Univ, Haikou, Peoples R China
[3] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China
关键词
Collaborative Topic Modeling; Developer Recommendation; Personalized Files Recommendation;
D O I
10.1109/COMPSAC.2016.101
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Given a new issue request, suitable developers should be arranged to implement it. Technologies such as developer recommendation have been proposed to tackle this issue. These techniques tend to recommend experienced developers, i.e., the more experienced the developer is, the more possible he/she is recommended. However, if the experienced developers are hectic, the junior developers may be employed to finish the incoming issue. But they may have difficulty in finishing these tasks for lack of developing experience. In this paper, we propose a novel approach, DR_PSF (Developer Recommendation with Personalized Source-code Files), to enhance developer recommendation by leveraging personalized source-code files. DR_PSF uses the collaborative topic modeling (CTM) technique to analyze developer expertise and triage some personalized files for the recommended developers. An empirical study is conducted, and the results show that DR_PSF can effectively recommend useful personalized source-code files for them to refer when they implement the incoming issue.
引用
收藏
页码:239 / 244
页数:6
相关论文
共 2 条
  • [1] Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code
    Huo, Xuan
    Li, Ming
    PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 1909 - 1915
  • [2] PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation
    Li, Zhihao
    Li, Chuanyi
    Tang, Ze
    Huang, Wanhong
    Ge, Jidong
    Luo, Bin
    Ng, Vincent
    Wang, Ting
    Hu, Yucheng
    Zhang, Xiaopeng
    ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 2024, 33 (03)