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Developer recommendation on bug commenting:a ranking approach for the developer crowd
by
Jifeng XUAN He JIANG Hongyu ZHANG Zhilei REN
in
bug
/ Collaboration
/ Computer Science
/ Cooperation
/ Debugging
/ Empirical analysis
/ Information Systems and Communication Service
/ Ranking
/ Research Paper
/ Tracking systems
/ 实证分析
/ 开发商
/ 开发者
/ 群体
/ 评论
/ 跟踪系统
/ 错误报告
2017
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Developer recommendation on bug commenting:a ranking approach for the developer crowd
by
Jifeng XUAN He JIANG Hongyu ZHANG Zhilei REN
in
bug
/ Collaboration
/ Computer Science
/ Cooperation
/ Debugging
/ Empirical analysis
/ Information Systems and Communication Service
/ Ranking
/ Research Paper
/ Tracking systems
/ 实证分析
/ 开发商
/ 开发者
/ 群体
/ 评论
/ 跟踪系统
/ 错误报告
2017
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Developer recommendation on bug commenting:a ranking approach for the developer crowd
by
Jifeng XUAN He JIANG Hongyu ZHANG Zhilei REN
in
bug
/ Collaboration
/ Computer Science
/ Cooperation
/ Debugging
/ Empirical analysis
/ Information Systems and Communication Service
/ Ranking
/ Research Paper
/ Tracking systems
/ 实证分析
/ 开发商
/ 开发者
/ 群体
/ 评论
/ 跟踪系统
/ 错误报告
2017
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Developer recommendation on bug commenting:a ranking approach for the developer crowd
Journal Article
Developer recommendation on bug commenting:a ranking approach for the developer crowd
2017
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Overview
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.
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