Name
Abstract | ||
|---|---|---|
AbstractBug resolution refers to the activity that developers perform to diagnose, fix, test, and document bugs during software development and maintenance. Given a bug report, we would like to recommend the set of bug resolvers that could potentially contribute their knowledge to fix it. We refer to this problem as developer recommendation for bug resolution. In this paper, we propose a new and accurate method named DevRec for the developer recommendation problem. DevRec is a composite method that performs two kinds of analysis: bug reports based analysis BR-Based analysis and developer based analysis D-Based analysis. We evaluate our solution on five large bug report datasets including GNU Compiler Collection, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 107,875 bug reports. We show that DevRec could achieve [email protected] and [email protected] scores of 0.4826-0.7989, and 0.6063-0.8924, respectively. The results show that DevRec on average improves [email protected] and [email protected] scores of Bugzie by 57.55% and 39.39%, outperforms DREX by 165.38% and 89.36%, and outperforms NonTraining by 212.39% and 168.01%, respectively. Moreover, we evaluate the stableness of DevRec with different parameters, and the results show that the performance of DevRec is stable for a wide range of parameters. Copyright © 2015 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1002/smr.1706 | Periodicals |
Keywords | Field | DocType |
developer recommendation,multi-label learning,topic model,composite | Data mining,Computer science,Multi label learning,Compiler,Eclipse,Topic model,Software development | Journal |
Volume | Issue | ISSN |
27 | 3 | 2047-7473 |
Citations | PageRank | References |
27 | 0.72 | 31 |
Authors | ||
4 | ||