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Automatic Voter Recommendation Method for Closing Questions in Stack Overflow
International Journal of Software Engineering and Knowledge Engineering ( IF 0.9 ) Pub Date : 2021-01-22 , DOI: 10.1142/s0218194020400276
Zhang Zhang 1, 2 , Xinjun Mao 1, 2 , Yao Lu 1, 2 , Jinyu Lu 1 , Yue Yu 1 , Zhixing Li 1
Affiliation  

Stack Overflow is the most popular programming question and answer community that continuously receives a large number of questions every day. To ensure the quality of questions, the community grants privileges for the moderators and a group of experienced users to review the quality of questions and close the low-quality ones (e.g. duplicate or irrelevant questions). The review process is a typical crowdsourcing job that relies on users’ volunteer participation, and the current practices of closing questions in Stack Overflow face two aspects of challenges: (1) an obvious increase in both the absolute number and the percentage of “closed” questions; (2) a considerable decrease in participation willingness of experienced users to close questions. In order to solve the problem, we present a novel model of user willingness for reviewing and voting questions by incorporating four types of user activity history, including questions, answers, comments and votes of closing questions. Then we propose an automatic recommendation method based on the model to assign experienced users proper questions, to utilize the forces of them to close questions. The evaluation shows that the successful recommendation probability in the top 5, top 10, top 20, top 30, top 40, top 50 users are 48.23%, 58.93%, 68.83%, 74.27%, 78.13% and 81%, respectively.

中文翻译:

Stack Overflow 中结束问题的自动选民推荐方法

Stack Overflow 是最受欢迎的编程问答社区,每天不断收到大量问题。为了保证问题的质量,社区授予版主和一群有经验的用户审查问题质量并关闭低质量问题(例如重复或不相关的问题)的权限。评审过程是典型的依赖用户自愿参与的众包工作,目前 Stack Overflow 关闭问题的做法面临两个方面的挑战:(1)“关闭”的绝对数量和百分比都明显增加问题; (2) 有经验的用户关闭问题的参与意愿显着下降。为了解决问题,我们通过结合四种类型的用户活动历史,包括问题、答案、评论和结束问题的投票,提出了一种新的用户对问题进行审查和投票的意愿模型。然后我们提出了一种基于模型的自动推荐方法,为有经验的用户分配适当的问题,利用他们的力量来关闭问题。评价结果显示,前5名、前10名、前20名、前30名、前40名、前50名用户的推荐成功概率分别为48.23%、58.93%、68.83%、74.27%、78.13%和81%。利用他们的力量来解决问题。评价表明,前5名、前10名、前20名、前30名、前40名、前50名用户的推荐成功概率分别为48.23%、58.93%、68.83%、74.27%、78.13%和81%。利用他们的力量来解决问题。评价表明,前5名、前10名、前20名、前30名、前40名、前50名用户的推荐成功概率分别为48.23%、58.93%、68.83%、74.27%、78.13%和81%。
更新日期:2021-01-22
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