Title

Dual Analysis for Recommending Developers to Resolve Bugs

Publication Type

Journal Article

Publication Date

3-2015

Abstract

Bug 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 recall@5 and recall@10 scores of 0.4826-0.7989, and 0.6063-0.8924, respectively. The results show that DevRec on average improves recall@5 and recall@10 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.

Keywords

Composite, Developer recommendation, Multi-label learning, Topic model

Discipline

Computer Sciences | Databases and Information Systems | Information Security

Research Areas

Data Management and Analytics; Software and Cyber-Physical Systems

Publication

Journal of Software: Evolution and Process

Volume

27

Issue

3

First Page

195

Last Page

220

ISSN

2047-7473

Identifier

10.1002/smr.1706

Publisher

Wiley: 12 months

Additional URL

http://dx.doi.org/10.1002/smr.1706

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