ISCAS OpenIR
FixerCache: Unsupervised caching active developers for diverse bug triage
Wang, Song (1); Zhang, Wen (2); Wang, Qing (3); Wang, Song
2014
会议名称8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2014
会议日期September 18, 2014 - September 19, 2014
会议地点Torino, Italy
收录类别EI
出版地IEEE Computer Society
ISSN19493770
ISBN9781450327749
部门归属(1) Institute of Software, Chinese Academy of Sciences, China; (2) State Key Laboratory of Software Engineering of Wuhan University, China; (3) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China
摘要Context: Bug triage aims to recommend appropriate developers for new bugs in order to reduce time and effort in bug resolution. Most previous approaches for bug triage are supervised. Before recommending developers, these approaches need to learn developers' bug-fix preferences via building and training models using text-information of developers' historical bug reports. Goal: In this paper, we empirically address three limitations of supervised bug triage approaches and propose FixerCache, an unsupervised approach for bug triage by caching developers based on their activeness in components of products. Method: In FixerCache, each component of a product has a dynamic developer cache which contains prioritized developers according to developers' activeness scores. Given a new bug report, FixerCache recommends fixers with high activeness in developer cache to participate in fixing the new bug. Results: Results of experiments on four products from Eclipse and Mozilla show that FixerCache outperforms supervised bug triage approaches in both prediction accuracy and diversity. And it can achieve prediction accuracy up to 96.32% and diversity up to 91.67%, with top-10 recommendation list. Conclusions: FixerCache recommends fixers for new bugs based on developers' activeness in components of products with high prediction accuracy and diversity. Moreover, since FixerCache does not need to learn developers' bug-fix preferences through complex and time consuming processes, it could reduce bug triage time from hours of supervised approaches to seconds.; Context: Bug triage aims to recommend appropriate developers for new bugs in order to reduce time and effort in bug resolution. Most previous approaches for bug triage are supervised. Before recommending developers, these approaches need to learn developers' bug-fix preferences via building and training models using text-information of developers' historical bug reports. Goal: In this paper, we empirically address three limitations of supervised bug triage approaches and propose FixerCache, an unsupervised approach for bug triage by caching developers based on their activeness in components of products. Method: In FixerCache, each component of a product has a dynamic developer cache which contains prioritized developers according to developers' activeness scores. Given a new bug report, FixerCache recommends fixers with high activeness in developer cache to participate in fixing the new bug. Results: Results of experiments on four products from Eclipse and Mozilla show that FixerCache outperforms supervised bug triage approaches in both prediction accuracy and diversity. And it can achieve prediction accuracy up to 96.32% and diversity up to 91.67%, with top-10 recommendation list. Conclusions: FixerCache recommends fixers for new bugs based on developers' activeness in components of products with high prediction accuracy and diversity. Moreover, since FixerCache does not need to learn developers' bug-fix preferences through complex and time consuming processes, it could reduce bug triage time from hours of supervised approaches to seconds.
语种英语
内容类型会议论文
URI标识http://ir.iscas.ac.cn/handle/311060/16631
专题中国科学院软件研究所
通讯作者Wang, Song
推荐引用方式
GB/T 7714
Wang, Song ,Zhang, Wen ,Wang, Qing ,et al. FixerCache: Unsupervised caching active developers for diverse bug triage[C]. IEEE Computer Society,2014.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Wang, Song (1)]的文章
[Zhang, Wen (2)]的文章
[Wang, Qing (3)]的文章
百度学术
百度学术中相似的文章
[Wang, Song (1)]的文章
[Zhang, Wen (2)]的文章
[Wang, Qing (3)]的文章
必应学术
必应学术中相似的文章
[Wang, Song (1)]的文章
[Zhang, Wen (2)]的文章
[Wang, Qing (3)]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。