A general noise-reduction framework for fault localization of Java programs

ISCAS OpenIR

	A general noise-reduction framework for fault localization of Java programs
	Xu, Jian (1); Zhang, Zhenyu (2); Chan, W.K. (3); Tse, T.H. (4); Li, Shanping (1); Zhang, Z.(zhangzy@ios.ac.cn)
	2013
页码	880-896
收录类别	SCI ; EI
出版地	Elsevier, P.O. Box 211, Amsterdam, 1000 AE, Netherlands
ISSN	9505849
部门归属	(1) Department of Computer Science, Zhejiang University, Hangzhou, China; (2) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; (3) Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Hong Kong, Hong Kong; (4) Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
摘要	Context: Existing fault-localization techniques combine various program features and similarity coefficients with the aim of precisely assessing the similarities among the dynamic spectra of these program features to predict the locations of faults. Many such techniques estimate the probability of a particular program feature causing the observed failures. They often ignore the noise introduced by other features on the same set of executions that may lead to the observed failures. It is unclear to what extent such noise can be alleviated. Objective: This paper aims to develop a framework that reduces the noise in fault-failure correlation measurements. Method: We develop a fault-localization framework that uses chains of key basic blocks as program features and a noise-reduction methodology to improve on the similarity coefficients of fault-localization techniques. We evaluate our framework on five base techniques using five real-life median-scaled programs in different application domains. We also conduct a case study on subjects with multiple faults. Results: The experimental result shows that the synthesized techniques are more effective than their base techniques by almost 10%. Moreover, their runtime overhead factors to collect the required feature values are practical. The case study also shows that the synthesized techniques work well on subjects with multiple faults. Conclusion: We conclude that the proposed framework has a significant and positive effect on improving the effectiveness of the corresponding base techniques. © 2012 Elsevier B.V. All rights reserved.; Context: Existing fault-localization techniques combine various program features and similarity coefficients with the aim of precisely assessing the similarities among the dynamic spectra of these program features to predict the locations of faults. Many such techniques estimate the probability of a particular program feature causing the observed failures. They often ignore the noise introduced by other features on the same set of executions that may lead to the observed failures. It is unclear to what extent such noise can be alleviated. Objective: This paper aims to develop a framework that reduces the noise in fault-failure correlation measurements. Method: We develop a fault-localization framework that uses chains of key basic blocks as program features and a noise-reduction methodology to improve on the similarity coefficients of fault-localization techniques. We evaluate our framework on five base techniques using five real-life median-scaled programs in different application domains. We also conduct a case study on subjects with multiple faults. Results: The experimental result shows that the synthesized techniques are more effective than their base techniques by almost 10%. Moreover, their runtime overhead factors to collect the required feature values are practical. The case study also shows that the synthesized techniques work well on subjects with multiple faults. Conclusion: We conclude that the proposed framework has a significant and positive effect on improving the effectiveness of the corresponding base techniques. © 2012 Elsevier B.V. All rights reserved.
关键词	Fault Localization Key Block Chain Noise Reduction Program Debugging
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16496
专题	中国科学院软件研究所
通讯作者	Zhang, Z.(zhangzy@ios.ac.cn)
推荐引用方式 GB/T 7714	Xu, Jian ,Zhang, Zhenyu ,Chan, W.K. ,et al. A general noise-reduction framework for fault localization of Java programs[C]. Elsevier, P.O. Box 211, Amsterdam, 1000 AE, Netherlands,2013:880-896.