Applying Deep Learning Based Automatic Bug Triager to Industrial Projects

被引：74

作者：

Lee, Sun-Ro ^{[1
]}

Heo, Min-Jae ^{[1
]}

Lee, Chan-Gun ^{[1
]}

Kim, Milhan ^{[2
]}

Jeong, Gaeul ^{[2
]}

机构：

[1] Chung Ang Univ, Seoul, South Korea

[2] LG Elect, Software Engn R&D Lab, Seoul, South Korea

来源：

ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING | 2017年

基金：

新加坡国家研究基金会;

关键词：

automatic bug triage; convolutional neural network; text classification; industrial project;

D O I：

10.1145/3106237.3117776

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：926 / 931

页数：6

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