Proceedings of the 11th International Conference on Quality Software (QSIC '11),
IEEE Computer Society Press, Los Alamitos, CA (2011)

A Dynamic Fault Localization Technique with Noise Reduction for Java Programs 1

Jian Xu 2 , W.K. Chan 3 , Zhenyu Zhang 4 , T.H. Tse 5 and Shanping Li 2

[paper from IEEE Xplore | paper from IEEE digital library | technical report TR-2011-09]


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 ignore the noise introduced by the other features on the same set of executions that may lead to the observed failures. In this paper, we propose both the use of chains of key basic blocks as program features and an innovative similarity coefficient that has noise reduction effect. We have implemented our proposal in a technique known as MKBC. We have empirically evaluated MKBC using three real-life medium-sized programs with real faults. The results show that MKBC outperforms Tarantula, Jaccard, SBI, and Ochiai significantly.

Keywords: debugging, testing, continuous integration

1. This research is supported in part by grants from the Natural Science Foundation of China (project nos. 61003027 and 61073006), grants from the General Research Fund of the Research Grants Council of Hong Kong (project nos. 111410 and 716507), and a strategy research grant of City University of Hong Kong (project no. 7008039).
2. Department of Computer Science, Zhejiang University, Hangzhou, China.
3. (Corresponding author.)
Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Hong Kong.
4. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China.
5. Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong.


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