Improved Usage Model for Web Application Reliability Testing

1. Vilkomir, S.A., Parnas, D.L., Mendiratta, V.B., Murphy, E.: Segregated Failures Model for Availability Evaluation of Fault-Tolerant Systems. In: 29th Australasian Computer Science Conference, vol. 48 (2006)

   Google Scholar 

2. Wang, W., Tang, M.: User-Oriented Reliability Modeling for a Web System. In: Proceedings of the 14th International Symposium on Software Reliability Engineering (ISSRE 2003), pp. 1–12 (2003)

   Google Scholar 

3. Whittaker, J.A., Thomason, M.G.: A Markov Chain Model for Statistical Software Testing. IEEE Trans. Software Eng. 20(10), 812–824 (1994)

   Article  Google Scholar 

4. Le Guen, H., Marie, R., Thelin, T.: Reliability Estimation for Statistical Usage Testing using Markov Chains. In: ISSRE 2004: Proceedings of the 15th International Symposium on Software Reliability Engineering, pp. 54–65. IEEE Computer Society, Washington, DC (2004)

   Google Scholar 

5. Dulz, W., Zhen, F.: MaTeLo—statistical usage testing by annotated sequence diagrams, Markov chains, and TTCN-3. In: Proceedings of Third International Conference On Quality Software (QSIC 2003). IEEE, Los Alamitos (2003)

   Google Scholar 

6. Sayre, K.: Improved Techniques for Software Testing Based on Markov Chain Usage Models. PhD thesis, University of Tennessee (1999)

   Google Scholar 

7. Borges, J.: A Data Mining Model to Capture User Web Navigation. PhD thesis, University College London, London Uiversity (2000)

   Google Scholar 

8. Borges, J., Levene, M.: A dynamic clustering-based Markov model for web usage mining. In: CoRR:the computing research repository. cs.IR/0406032 (2004)

   Google Scholar 

9. Goseva-Popstojanova, K., Trivedi, K.S.: Failure Correlation in Software Reliability Models. IEEE Trans. on Reliability 49, 37–48 (2000)

   Article  Google Scholar 

10. Goseva-Popstojanova, K., Hamill, M.: Estimating the Probability of Failure When Software Runs Are Dependent: An Empirical Study. In: 20th International Symposium on Software Reliability Engineering, ISSRE, pp. 21–30 (2009)

    Google Scholar 

11. Feliachi, A., Le Guen, H.: Generating transition probabilities for automatic model-based test generation. In: Third International Conference on Software Testing, Verification and Validation, pp. 99–102 (2010)

    Google Scholar 

12. Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Knowledge and Information Systems 1(1), 5–32 (1999)

    Google Scholar 

13. Pei, J., et al.: Mining Access Patterns Efficiently from Web Logs. In: Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pp. 396–407. Springer, New York (2000)

    Google Scholar 

14. Miller, K.W., et al.: Estimating the Probability of Failure When Testing Reveals No Failures. IEEE Transactions on Software Engineering 18, 33–42 (1992)

    Article  Google Scholar 

15. Pirolli, P.L.T., Pitkow, J.E.: Distributions of surfers’ paths through the world wide web: Empirical characterizations. World Wide Web, 29–45 (1999)

    Google Scholar 

16. Deshpande, M., Karypis, G.: Selective Markov Models for Predicting Web-Page Accesses. In: Proc. of the 1st SIAM International Conference on Data Mining (2001)

    Google Scholar 

17. Charniak, E.: Statistical Language Learning. The MIT Press, Cambridge (1996)

    Google Scholar 

18. Jespersen, S., Pedersen, T.B., Thorhauge, J.: Evaluating the markov assumption for web usage mining. In: Proceeding of the Fifth International Workshop on Web Information and Data Management (WIDM 2003), pp. 82–89 (2003)

    Google Scholar 

19. Walpole, R.E., Myers, R.H.: Probability and Statistics for Engineers and Scientists, 5th edn. Macmillan Publishing Company, Basingstoke (1993)

    Google Scholar 

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