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      Author
      Liu, Kaiping
      Tan, Hee Beng Kuan
      Chandramohan, Mahinthan
      Date of Issue
      2012
      Conference Name
      International Symposium on the Foundations of Software Engineering (20th : 2012 : Cary, USA)
      School
      School of Computer Engineering
      Abstract
      Bug reporting is an uncoordinated process that is often the cause of redundant workload in triaging and fixing bugs due to many duplicated bug reports. Furthermore, quite often, same bugs are repeatedly reported as users or testers are unaware of whether they have been reported from the search query results. In order to reduce both the users and developers' efforts, the quality of search in a bug tracking system is crucial. However, all existing search functions in a bug tracking system produce results with undesired relevance and ranking. Hence, it is essential to provide an effective search function to any bug tracking system. Learning to rank (LTR) is a supervised machine learning technique that is used to construct a ranking model from training data. We propose a novel approach by using LTR to search for potentially related bug reports in a bug tracking system. Our method uses a set of proposed features of bug reports and queries. A preliminary evaluation shows that our approach can enhance the quality of searching for similar bug reports, therefore, relieving the burden of developers in dealing with duplicate bug reports.
      Type
      Conference Paper
      Collections
      • SCSE Conference Papers
      http://dx.doi.org/10.1145/2393596.2393628
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