Name
Abstract | ||
|---|---|---|
Bug reporting is essentially an uncoordinated process. The same bugs could be repeatedly reported because users or testers are unaware of previously reported bugs. As a result, extra time could be spent on bug triaging and fixing. In order to reduce redundant effort, it is important to provide bug reporters with the ability to search for previously reported bugs. The search functions provided by the existing bug tracking systems are using relatively simple ranking functions, which often produce unsatisfactory results. In this paper, we adopt Ranking SVM, a Learning to Rank technique to construct a ranking model for effective bug report search. We also propose to use the knowledge of Wikipedia to discover the semantic relations among words and documents. Given a user query, the constructed ranking model can search for relevant bug reports in a bug tracking system. Unlike related works on duplicate bug report detection, our approach retrieves existing bug reports based on short user queries, before the complete bug report is submitted. We perform evaluations on more than 16,340 Eclipse and Mozilla bug reports. The evaluation results show that the proposed approach can achieve better search results than the existing search functions provided by Bugzilla and Lucene. We believe our work can help users and testers locate potential relevant bug reports more precisely. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/WCRE.2013.6671283 | SIGSOFT FSE |
Keywords | DocType | Citations |
bug report search,wikipedia,semantic relation discovery,bug reporting,bug report,bug tracking system,bug triaging,learning (artificial intelligence),user query,similar bug report,duplicate bug report,existing search function,eclipse bug reports,semantic relation,bugzilla,mozilla bug reports,program debugging,bug tracking systems,documents,related bug report,ranking functions,search quality,learning to rank technique,web sites,effective search function,search query result,bug fixing,document handling,bug reporters,novel approach,support vector machines,ranking svm,query processing,lucene,search engine,learning to rank | Conference | 0 |
PageRank | References | Authors |
0.34 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaiping Liu | 1 | 13 | 5.33 |
| Hee Beng Kuan Tan | 2 | 489 | 45.05 |
| Mahinthan Chandramohan | 3 | 222 | 11.67 |