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Applying Deep Learning Based Automatic Bug Triager to Industrial Projects
- Authors
- Lee, Sun-Ro; Heo, Min-Jae; Lee, Chan-Gun; Kim, Milhan; Jeong, Gaeul
- Issue Date
- Aug-2017
- Publisher
- ASSOC COMPUTING MACHINERY
- Keywords
- automatic bug triage; convolutional neural network; text classification; industrial project
- Citation
- ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING, v.Part F130154, pp 926 - 931
- Pages
- 6
- Journal Title
- ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING
- Volume
- Part F130154
- Start Page
- 926
- End Page
- 931
- ISSN
- 0000-0000
- Abstract
- Finding the appropriate developer for a bug report, so called 'Bug Triage', is one of the bottlenecks in the bug resolution process. To address this problem, many approaches have proposed various automatic bug triage techniques in recent studies. We argue that most previous studies focused on open source projects only and did not consider deep learning techniques. In this paper, we propose to use Convolutional Neural Network and word embedding to build an automatic bug triager. The results of the experiments applied to both industrial and open source projects reveal benefits of the automatic approach and suggest co-operation of human and automatic triagers. Our experience in integrating and operating the proposed system in an industrial development environment is also reported.
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