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|Title:||Has this bug been reported?||Authors:||Liu, Kaiping
Tan, Hee Beng Kuan
|Issue Date:||2012||Source:||Liu, K., Tan, H. B. K., & Chandramohan, M. (2012). Has this bug been reported? Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering - FSE '12.||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.||URI:||https://hdl.handle.net/10356/98832
|DOI:||10.1145/2393596.2393628||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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