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TagCombine： Recommending Tags to Contents in Software Information Sites

Journal of Computer Science & Technology 2015年第5期30卷 1017-1035页

作者：王新宇夏鑫 david lo 1.College of Computer Science and Technology Zhejiang University Hangzhou 310027 China 2.School of Information Systems Singapore Management University Singapore Singapore

Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three different components： 1） multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2） similarity-based ranking component which recommends tags from similar objects; 3） tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Feecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recallS10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.

关键词： software information site online media tag recommendation

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Multi-Factor Duplicate Question Detection in Stack Overflow

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Journal of Computer Science & Technology 2015年第5期30卷 981-997页

作者：张芸 david lo 夏鑫孙建伶 College of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Information Systems Singapore Management University Singapore Singapore

Stack Overflow is a popular on-line question and answer site for software developers to share their experience and expertise. Among the numerous questions posted in Stack Overflow, two or more of them may express the same point and thus are duplicates of one another. Duplicate questions make Stack Overflow site maintenance harder, waste resources that could have been used to answer other questions, and cause developers to unnecessarily wait for answers that are already available. To reduce the problem of duplicate questions, Stack Overflow allows questions to be manually marked as duplicates of others. Since there are thousands of questions submitted to Stack Overflow every day, manually identifying duplicate questions is a difficult work. Thus, there is a need for an automated approach that can help in detecting these duplicate questions. To address the above-mentioned need, in this paper, we propose an automated approach named DuPPREDICTOR that takes a new question as input and detects potential duplicates of this question by considering multiple factors. DuPPREDICTOR extracts the title and description of a question and also tags that are attached to the question. These pieces of information （title, description, and a few tags） are mandatory information that a user needs to input when posting a question. DuPPREDICTOR then computes the latent topics of each question by using a topic model. Next, for each pair of questions, it computes four similarity scores by comparing their titles, descriptions, latent topics, and tags. These four similarity scores are finally combined together to result in a new similarity score that comprehensively considers the multiple factors. To examine the benefit of DuPPREDICTOR, we perform an experiment on a Stack Overflow dataset which contains a total of more than two million questions. The result shows that DuPPREDICTOR can achieve a recali-rate@20 score of 63.8%. We compare our approach with the standard search engine of Stack Overfl

关键词： software information site duplicate question Stack Overflow DupPredictor

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