Fault Detection Technique Based on Fine Grained Slicing Spectrum

Xi Guo

doi:10.4028/www.scientific.net/AMM.543-547.963

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Fault Detection Technique Based on Fine Grained...

Fault Detection Technique Based on Fine Grained Slicing Spectrum

Abstract:

Test coverage information is usually used to compute the suspiciousness to locate the software errors in the current fault localization techniques, but this technique usually do not consider the reliance information within the target program, and the precision is also very low. A novel fault localization technique based on fine grained slicing spectrum is proposed in this paper, which can increase the efficiency of fault localization. This technique analyzes the reliance information under fine grained level, and selects the check points which are prone to be faulty, and the faulty statements is located according to the suspicious result. Experimental results show that this technique has better efficiency than the current techniques.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

963-966

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.963

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Online since:

March 2014

Authors:

Xi Guo*

Keywords:

Fault Localization, Program Spectrum, Slicing Spectrum, Suspiciousness

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* - Corresponding Author

References

[1] Zeller A, Hildebrandt R. Simplifying and isolating failure inducing input. IEEE Trans on Software Engineering(2002).

DOI: 10.1109/32.988498

Google Scholar

[2] Cleve H, Zeller A, Locating causes of program failures. Proc. of the Int. conf. on Software Engineering(2005).

Google Scholar

[3] Jeffrey D, Gupta N. Gupta R. Fault localization using value replacement. Proc. of the Int. Symp. On Software Testing and Analysis(2008).

DOI: 10.1145/1390630.1390652

Google Scholar

[4] Jeffrey D, Gupta N, Gupta R. Effective and efficient localization of multiple faults sing value replacement. Proc. of the IEEE Int. Conf. on Software Maintenance(2009).

DOI: 10.1109/icsm.2009.5306303

Google Scholar

[5] Jones J, Harrold M. Empirical evaluation of the tarantula automatic fault localization technique. Proc. of the 24th Int. Conf. on Software Engineering(2002).

Google Scholar

[6] Wanzhi W, Bixin L, Xiaobing S, et al. A technique of multiple fault localization based on conditioned execution slicing spectrum. Journal of Computer Research and Development(2013).

Google Scholar

[7] Abreu R, Zoeteweij P, et al. On the accuracy of spectrum-based fault localization. Proc. of Testing: Academic and Industrial Conference-Practice and Research Technique(2007).

DOI: 10.1109/taic.part.2007.13

Google Scholar

[8] Abreu R, Zoeteweij P, et al. An evaluation of similarity coefficients for software fault localization. Proc. of the 12th IEEE Pracific Rim Int. Symp. On Dependable Computing(2006).

DOI: 10.1109/prdc.2006.18

Google Scholar