mining associations to improve the effectiveness of fault localization

ISCAS OpenIR

	mining associations to improve the effectiveness of fault localization
	Zhao Lei; Wang Li-Na; Gao Dong-Ming; Zhang Zhen-Yu; Xiong Zuo-Ting
	2012
发表期刊	Jisuanji Xuebao/Chinese Journal of Computers
ISSN	0254-4164
卷号	35 期号:12 页码:2528-2540
摘要	Coverage-based fault localization (CBFL) techniques find the fault-related positions in programs by comparing the execution statistics of passed executions and failed executions have been proven to be efficient by several empirical studies. However, these techniques assess the suspiciousness of program entities individually, whereas the individual coverage information cannot reflect the complicated control- and data-dependency relationships, and thus oversimplify the execution spectra. In this paper, we first use motivating examples to show the impact of the cause-effect relationship on the effectiveness of CBFL. Second, we propose the rules of program failures and design the execution analysis model based on the coverage of different program execution spectrum. By computing the frequency items for statements with high suspiciousness, we also bring out the coverage vector to mine fault-prone statements. The controlled experiments based on the SIR benchmarks indicate that our technique is promising.; Coverage-based fault localization (CBFL) techniques find the fault-related positions in programs by comparing the execution statistics of passed executions and failed executions have been proven to be efficient by several empirical studies. However, these techniques assess the suspiciousness of program entities individually, whereas the individual coverage information cannot reflect the complicated control- and data-dependency relationships, and thus oversimplify the execution spectra. In this paper, we first use motivating examples to show the impact of the cause-effect relationship on the effectiveness of CBFL. Second, we propose the rules of program failures and design the execution analysis model based on the coverage of different program execution spectrum. By computing the frequency items for statements with high suspiciousness, we also bring out the coverage vector to mine fault-prone statements. The controlled experiments based on the SIR benchmarks indicate that our technique is promising.
收录类别	EI
关键词	Hardware Software Engineering
部门归属	(1) Key Laboratory of Aerospace Information Security and Trust Computing Ministry of Education Wuhan 430072 China; (2) School of Computer Wuhan University Wuhan 430072 China; (3) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China
语种	中文
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15330
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhao Lei,Wang Li-Na,Gao Dong-Ming,et al. mining associations to improve the effectiveness of fault localization[J]. Jisuanji Xuebao/Chinese Journal of Computers,2012,35(12):2528-2540.
APA	Zhao Lei,Wang Li-Na,Gao Dong-Ming,Zhang Zhen-Yu,&Xiong Zuo-Ting.(2012).mining associations to improve the effectiveness of fault localization.Jisuanji Xuebao/Chinese Journal of Computers,35(12),2528-2540.
MLA	Zhao Lei,et al."mining associations to improve the effectiveness of fault localization".Jisuanji Xuebao/Chinese Journal of Computers 35.12(2012):2528-2540.