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

Science China(Information Sciences) 2017年第7期60卷 159-176页

作者： Jifeng XUAN He JIANG Hongyu ZHANG Zhilei REN State Key Laboratory of Software Engineering School of Computer Wuhan University School of Software Dalian University of Technology Microsoft Research Asia School of Electrical Engineering and Computer Science The University of Newcastle

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

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Effectiveness of exploring historical commits for developer recommendation： an empirical study

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Frontiers of Computer Science 2018年第3期12卷 528-544页

作者： Xiaobing SUN Hui YANG Hareton LEUNG Bin LI Hanchao (Jerry) LI Lingzhi LIAO School of Information Engineering Yangzhou University Yangzhou 225127 China Information Technology Research Base of Civil Aviation Administration of China Civil Aviation University of China Tianjin 300300 China Department of Computing The Hong Kong Polytechnic University Hong Kong China Coventry University Coventry CVI 5FB UK State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Nanjing University of Information Science and Technology Nanjing 210044 China

developer recommendation is an essential task for resolving incoming issues in the evolution of software. Many developer recommendation techniques have been developed in the literature; among these studies, most techniques usually combined historical commits as supplementary information with bug repositories and/or source-code repositories to recommend developers. However, the question of whether the messages in historical commits are always useful has not yet been answered. This article aims at solving this problem by conducting an empirical study on four open-source projects. The results show that：（1） the number of meaningful words of the commit description has an impact on the quality of the commit, and a larger number of meaningful words in the description means that it can generally better reflect developers＇ expertise; （2） using commit description to recommend the relevant developers is better than that using relevant files that are recorded in historical commits; （3） developers tend to change the relevant files that they have changed many times before; （4） developers generally tend to change the files that they have changed recently.

关键词： developer recommendation historical commits empirical study

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Automated Time Based Multi-Criteria Bug Triage Approach:developer Working Efficiency and Social Network Based developer recommendation

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 566-578页

作者： YADAV Asmita SINGH Kumar Sandeep Krishna Engineering College Ghaziabad 201007India JIIT Noida 201309India

In software development projects,bugs are common *** report bugs in open source *** is a need to develop high quality developer prediction model that considers developer work satisfaction,keep within limited development cost,and improve bug resolution *** address and resolve bug report as soon as possible is the main focus of triager when a new bug is ***,developer work efficiency is an important factor in *** address these issues,a proposed approach recommends a set of developers that could potentially share their knowledge with each other to fix new bug *** proposed approach is called developer working efficiency and social network based developer recommendation(DweSn).It is a composite model that builds developers'profile by using developer average bug fixing time,work efficiency to fix variety of bugs,as well as the developer's social interactions with other developers.A similarity measure is applied between new bug and bugs in corpus to extract the list of capable developers from the *** proposed approach only selects those developers who are active and less loaded with *** developer with the highest profile score is assigned the *** evaluated our approach on the subset of five large open-source projects including Mozilla,Netbeans,Eclipse,Firefox and OpenOffice,and compared it with the *** results demonstrate that combination of developers'efficiency with their average bug fixing time and interactions in their social network gives good accuracy and efficiently reduces bug tossing *** approach shows an improvement in prediction accuracy,precision,recall,F-score and reduced bug tossing length up to 93.89%,93.12%,93.46%,93.27%and 93.25%,*** proposed approach achieved a 93%hit ratio and 93.34%mean reciprocal rank,indicating that our proposed triager is able to efficiently assign bugs to correct developers.

关键词： open-source repository bug report bug assignment tossing length bug triager developer social network developer recommendation

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Geek Talents:Who are the Top Experts on GitHub and Stack Overflow?

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Computers, Materials & Continua 2019年第8期61卷 465-479页

作者： Yijun Tian Waii Ng Jialiang Cao Suzanne McIntosh New York University Courant Institute of Mathematical SciencesNew York10012USA

In the field of Computer Science,software developers need to use a wide array of social collaborative platforms for learning and *** most popular ones are GitHub and Stack *** platforms only support search queries to extract relevant repository information from GitHub,or questions and answers from Stack *** ignores the valuable coder-related part-who are the top experts(geek talents)in a specific area?This information is important to companies,open source projects,and to those who want to learn from an expert role ***,how to find the right developers is quite a crucial yet challenging *** of the current works mainly focus on recommending experts in a particular software engineering task and ignore the relationship between developers within different *** this paper,we propose a novel technique that automatically identifies geek talents from GitHub,Stack Overflow,and across both *** results show that our work performs well at recommending proper developers in diverse areas.

关键词： developer recommendation collaborative filtering stack overflow GitHub

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