Dynamic Fault Localization in Web Application

Authors

  • Kumar Akula V
  • Chaitanya Kumar N

Keywords:

The word Coefficient, Suspiciousness and Prediction are one are the same, fault, faulty statement, malformed statement

Abstract

Now-a-days, web applications have become prevalent around the world. This technology has made it possible to carry on business along the web. Many companies developing web applications. There is a need of locating faults in web applications. In this report, we present a technique to locate faults in web applications. Dynamic Fault Localization is a process to localize the faulty statements in web application programs. To locate faulty statement in web programs we implement Tarantula, Ochiai, and Jaccard Coefficients.

References

Analysing and Testing Web Application Performance Research Inventy: International Journal of Engineering and Science Vol.3, Issue 10 (October 2013), PP 47-50

Improving Browsing Environment Compliance Evaluations for Websites Cyntrica Eaton 4166 A. V. Williams Building Department of Computer Science University of Maryland,

Bo Jiang, Zhenyu Zhang, T.H. Tse, T.Y. Chen, “How Well Do Test Case Prioritization Technique Support Statistical Fault Localization”, International Computer Software and Applications Conference, 2009.

Fault Localization for Dynamic Web Applications, IEEE Transactions On Software Engineering, Vol. 38, No. 2, March/April 2012.

Eric Wong, Vidroha Debroy, “ Software Fault Localization” W. IEEE Annual Technology Report, 2009.

http://www.w3.org/TR/html4/

H. Pan and E. H. Spafford. Heuristics for automatic localization of software faults. Technical Report SERC-TR-116-P, Purdue University, July 1992.

T. Reps, T. Ball, M. Das, and J. Larus, “The Use of Program Profiling for Software Maintenance with Applications to the Year 2000 Problem,” in Proceedings of the 6th European Software Engineering Conference, pp. 432-449, Zurich, Switzerland, September, 1997

M. J. Harrold, G. Rothermel, K. Sayre, R. Wu, and L. Yi, “An Empirical Investigation of the Relationship between Spectra Differences and Regression Faults,” Journal of Software Testing, Verification and Reliability, 10(3):171-194, September 2000

M. Weiser, “Program slicing,” IEEE Transactions on Software Engineering, SE-10(4):352-357, July 1984

J.A. Jones and M.J. Harrold, “Empirical Evaluation of the Tarantula Automatic Fault-Localization Technique,” Proc. IEEE/ ACM Int’l Conf. Automated Software Eng., pp. 273-282, 2005.

J.A. Jones, M.J. Harrold, and J. Stasko, “Visualization of Test Information to Assist Fault Localization,” Proc. Int’l Conf. Software Eng., pp. 467-477, 2002.

A.K. Jain and R.C. Dubes, Algorithms for Clustering Data. Prentice- Hall, Inc., 1988.

M.Y. Chen, E. Kiciman, E. Fratkin, A. Fox, and E. Brewer, “Pinpoint: Problem Determination in Large, Dynamic Internet Services,” Proc. Int’l Conf. Dependable Systems and Networks, pp. 595-604, 2002

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Published

2014-09-30

How to Cite

[1]
V. Kumar Akula and N. Chaitanya Kumar, “Dynamic Fault Localization in Web Application”, Int. J. Comp. Sci. Eng., vol. 2, no. 9, pp. 44–49, Sep. 2014.

Issue

Vol. 2 No. 9 (2014): IJCSE September Edition

Section

Research Article

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