Applying Deep Learning Based Automatic Bug Triager to Industrial Projects
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sun-Ro | - |
dc.contributor.author | Heo, Min-Jae | - |
dc.contributor.author | Lee, Chan-Gun | - |
dc.contributor.author | Kim, Milhan | - |
dc.contributor.author | Jeong, Gaeul | - |
dc.date.accessioned | 2022-04-14T09:40:15Z | - |
dc.date.available | 2022-04-14T09:40:15Z | - |
dc.date.issued | 2017-08 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56536 | - |
dc.description.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. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | Applying Deep Learning Based Automatic Bug Triager to Industrial Projects | - |
dc.type | Article | - |
dc.identifier.doi | 10.1145/3106237.3117776 | - |
dc.identifier.bibliographicCitation | ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING, v.Part F130154, pp 926 - 931 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000414279300091 | - |
dc.identifier.scopusid | 2-s2.0-85030776788 | - |
dc.citation.endPage | 931 | - |
dc.citation.startPage | 926 | - |
dc.citation.title | ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING | - |
dc.citation.volume | Part F130154 | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | automatic bug triage | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | text classification | - |
dc.subject.keywordAuthor | industrial project | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.