Developer recommendation on bug commenting：a ranking approach for the developer crowd  

作　　者：Jifeng XUAN He JIANG Hongyu ZHANG Zhilei REN 

机构地区：[1]State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China [2]School of Software, Dalian University of Technology, Dalian 116621, China [3]Microsoft Research Asia, Beijing 100080, China [4]School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan NSW 2308, Australia

出　　处：《Science China(Information Sciences)》2017年第7期155-172,共18页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61502345, 61403057, 61370144, 61272089);New Century Excellent Talents in University (Grant No. NCET13-0073)

摘　　要：A bug tracking system provides a collaborative platform for the developer crowd. After a bug report is submitted, developers can make comments to supplement the details of the bug report. Due to the large number of developers and bug reports, it is hard to determine which developer（also called commenter） is able to comment on a particular bug report. We refer to the problem of recommending developers for commenting on bug reports as commenter recommendation. In this paper, we perform an empirical analysis on commenter recommendation based on five-year bug reports of four open source projects. First, we preliminarily analyze bug comments and commenters in three categories, the relationship between commenters and fixers, the data scale of comments, and the collaboration on bug commenting. Second, we design a recommendation approach via ranking developers in the crowd to reduce the manual effort of identifying commenters. In this approach,we formulize the commenter recommendation problem as a multi-label recommendation task and leverage both developer collaboration and bug content to find out appropriate commenters. Experimental results show that our approach can effectively recommend commenters; 41% to 75% of the recall value is achieved for top-10 recommendation. Our empirical analysis on bug commenting can help developers understand and improve the process of fixing bugs.A bug tracking system provides a collaborative platform for the developer crowd. After a bug report is submitted, developers can make comments to supplement the details of the bug report. Due to the large number of developers and bug reports, it is hard to determine which developer（also called commenter） is able to comment on a particular bug report. We refer to the problem of recommending developers for commenting on bug reports as commenter recommendation. In this paper, we perform an empirical analysis on commenter recommendation based on five-year bug reports of four open source projects. First, we preliminarily analyze bug comments and commenters in three categories, the relationship between commenters and fixers, the data scale of comments, and the collaboration on bug commenting. Second, we design a recommendation approach via ranking developers in the crowd to reduce the manual effort of identifying commenters. In this approach,we formulize the commenter recommendation problem as a multi-label recommendation task and leverage both developer collaboration and bug content to find out appropriate commenters. Experimental results show that our approach can effectively recommend commenters; 41% to 75% of the recall value is achieved for top-10 recommendation. Our empirical analysis on bug commenting can help developers understand and improve the process of fixing bugs.

关 键 词：developer recommendation bug comments empirical analysis recommendation for the crowd collaborative filtering software repositories 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]