FixerCache: Unsupervised caching active developers for diverse bug triage

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
ISSN	19493770
ISBN	9781450327749
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.