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Personalized Reliability Prediction of Web Services

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Abstract

Service Oriented Architecture (SOA) is a business-centric IT architectural approach for building distributed systems. Reliability of service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet connections. Designing efficient and effective reliability prediction approaches of Web services has become an important research issue. In this article, we propose two personalized reliability prediction approaches of Web services, that is, neighborhood-based approach and model-based approach. The neighborhood-based approach employs past failure data of similar neighbors (either service users or Web services) to predict the Web service reliability. On the other hand, the model-based approach fits a factor model based on the available Web service failure data and use this factor model to make further reliability prediction. Extensive experiments are conducted with our real-world Web service datasets, which include about 23 millions invocation results on more than 3,000 real-world Web services. The experimental results show that our proposed reliability prediction approaches obtain better reliability prediction accuracy than other competing approaches.

References

  1. ANSI/IEEE. 1991. Standard glossary of software engineering terminology. ANSI/IEEE STD-729-1991.Google ScholarGoogle Scholar
  2. Bertolino, A. and Polini, A. 2009. SOA test governance: Enabling service integration testing across organization and technology borders. In Proceedings of the IEEE International Conference on Software Testing, Verification, and Validation Workshops. 277--286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Breese, J. S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI’98). 43--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Burke, R. 2002. Hybrid recommender systems: Survey and experiments. User Model. User-Adap. Interact. 12, 4, 331--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cardoso, J., Miller, J., Sheth, A., and Arnold, J. 2002. Modeling quality of service for workflows and web service processes. J. Web Semant. 1, 281--308.Google ScholarGoogle ScholarCross RefCross Ref
  6. Cheung, L., Roshandel, R., Medvidovic, N., and Golubchik, L. 2008. Early prediction of software component reliability. In Proceedings of the 30th International Conference on Software Engineering (ICSE’08). 111--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Cheung, R. C. 1980. A user-oriented software reliability model. IEEE Trans. Softw. Eng. 6, 2, 118--125. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Cortellessa, V. and Grassi, V. 2007a. A modeling approach to analyze the impact of error propagation on reliability of component-based systems. In Proceedings of the 10th International Symposium on Component Based Software Engineering (CBSE’07). 140--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Cortellessa, V. and Grassi, V. 2007b. Reliability modeling and analysis of service-oriented architectures. In Test and Analysis of Web Services, 339--362.Google ScholarGoogle Scholar
  10. Filieri, A., Ghezzi, C., Grassi, V., and Mirandola, R. 2010. Reliability analysis of component-based systems with multiple failure modes. In Proceedings of the 13th International Symposium on Component Based Software Engineering (CBSE’10). 1--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Friedman, M., Tran, P., and Goddard, P. 1992. Reliability techniques for combined hardware and software systems. Tech. rep. RL-TR-92-15, Rome Laboratory.Google ScholarGoogle Scholar
  12. Gokhale, S. S. and Lyu, M. R.-T. 2005. A simulation approach to structure-based software reliability analysis. IEEE Trans. Softw. Eng. 31, 643--656. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Gokhale, S. S. and Trivedi, K. S. 2002. Reliability prediction and sensitivity analysis based on software architecture. In Proceedings of the International Symposium on Software Reliability Engineering (ISSRE’02). 64--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Golub, G. H. and Loan, C. F. V. 1996. Matrix Computations 3rd Ed. The Johns Hopkins University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Goseva-Popstojanova, K. and Trivedi, K. S. 2001. Architecture-based approach to reliability assessment of software systems. Perf. Eval. 45, 2--3, 179--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Goseva-Popstojanova, K., Hassan, A., Abdelmoez, W., Nassar, D. E. M., Ammar, H., and Mili, A. 2003. Architectural-level risk analysis using uml. IEEE Trans. Softw. Eng. 29, 10, 946--960. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Grassi, V. and Patella, S. 2006. Reliability prediction for service-oriented computing environments. IEEE Internet Comput. 10, 3, 43--49. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Hamlet, D. 2009. Tools and experiments supporting a testing-based theory of component composition. ACM Trans. Softw. Eng. Methodol. 18, 12:1--12:41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Herlocker, J. L., Konstan, J. A., and Riedl, J. 2000. Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW’00). 241--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Hofmann, T. 2004. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22, 1, 89--115. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Huang, C.-Y., Lyu, M., and Kuo, S.-Y. 2003. A unified scheme of some nonhomogenous poisson process models for software reliability estimation. IEEE Trans. Soft. Eng. 29, 3, 261--269. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Jelinski, Z. and Moranda, P. 1972. Software reliability research. In Proceedings of the Statistical Methods for the Evaluation of Computer System Performance. 465--484.Google ScholarGoogle Scholar
  23. Koziolek, H., Schlich, B., and Bilich, C. 2010. A large-scale industrial case study on architecture-based software reliability analysis. In Proceedings of the 21st IEEE International Symposium on Software Reliability Engineering (ISSRE’10). 279--288. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Littlewood, B., Abdel-Ghaly, A., and Chan, P. 1986. Tools for the Analysis of the Accuracy of Software Reliability Predictions. Springer-Verlag, Heidelberg.Google ScholarGoogle Scholar
  25. Lyu, M. and Nikora, A. 1992. Applying reliability models more effectively {software}. Software, IEEE 9, 4, 43--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Lyu, M. R. 1995. Software Fault Tolerance. Trends in Software, Wiley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Lyu, M. R. 1996. Handbook of Software Reliability Engineering. McGraw-Hill, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Lyu, M. R. 2007. Software reliability engineering: A roadmap. Prof. Future of Software Engineering, Int’l Conf. Software Engineering (ICSE’07) 0, 153--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Ma, H., King, I., and Lyu, M. R. 2007. Effective missing data prediction for collaborative filtering. In Proceedings of the 30th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’07). 39--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Ma, H., King, I., and Lyu, M. R. 2009. Learning to recommend with social trust ensemble. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’09). 203--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Musa, J. D. 1993. Operational profiles in software-reliability engineering. IEEE Softw. 10, 14--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Musa, J. D., Iannino, A., and Okumoto, K. 1990. Software Reliability: Measurement, Prediction, Application. McGraw-Hill, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Putnam, L. H. and Myers, W. 1992. Measures for Excellence: Reliable Software on Time, Within Budget. Prentice-Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Rennie, J. D. M. and Srebro, N. 2005. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd International Conference on Machine Learning (ICML’05). 713--719. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work. 175--186. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Reussner, R. H., Schmidt, H. W., and Poernomo, I. H. 2003. Reliability prediction for component-based software architectures. J. Syst. Softw. 66, 3, 241--252. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Salakhutdinov, R. and Mnih, A. 2007. Probabilistic matrix factorization. In Proceedings of the Advances in Neural Information Processing Systems. 1257--1264.Google ScholarGoogle Scholar
  38. Salakhutdinov, R. and Mnih, A. 2008. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proceedings of the 25th International Conference on Machine Learning (ICML’08). 880--887. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW’01). 285--295. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Scholz, A., Buckl, C., Kemper, A., Knoll, A., Heuer, J., and Winter, M. 2008. Ws-amuse - web service architecture for multimedia services. In Proceedings of the 30th International Conference on Software Engineering (ICSE’08). 703--712. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., and Mei, H. 2007. Personalized qos prediction for web services via collaborative filtering. In Proceedings of the 5th International Conference on Web Services (ICWS’07). 439--446.Google ScholarGoogle Scholar
  42. Shardanand, U. and Maes, P. 1995. Social information filtering: Algorithms for automating word of mouth. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. ter Beek, M. H., Gnesi, S., Koch, N., and Mazzanti, F. 2008. Formal verification of an automotive scenario in service-oriented computing. In Proceedings of the 30th International Conference on Software Engineering (ICSE’08). 613--622. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Tsai, W. T. 2005. Service-oriented system engineering: A new paradigm. In Proceedings of the IEEE International Workshop on Service-Oriented System Engineering (SOSE’05). 3--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Tsai, W.-T., Zhou, X., Chen, Y., and Bai, X. 2008. On testing and evaluating service-oriented software. IEEE Comput. 41, 8, 40--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Wang, J., de Vries, A. P., and Reinders, M. J. 2006a. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’06). 501--508. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Wang, W.-L., Pan, D., and Chen, M.-H. 2006b. Architecture-based software reliability modeling. J. Syst. Softw. 79, 1, 132--146. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Xing, F., Guo, P., and Lyu, M. 2005. A novel method for early software quality prediction based on support vector machine. In Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering (ISSRE’05). 213--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Xue, G., Lin, C., Yang, Q., Xi, W., Zeng, H., Yu, Y., and Chen, Z. 2005. Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). 114--121. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Yacoub, S. M., Cukic, B., and Ammar, H. H. 1999. Scenario-based reliability analysis of component-based software. In Proceedings of the International Symposium on Software Reliability Engineering (ISSRE’99). 22--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Ye, C., Cheung, S. C., and Chan, W. K. 2006. Publishing and composition of atomicity-equivalent services for B2B collaboration. In Proceedings of the 28th International Conference on Software Engineering (ICSE’06). 351--360. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Yu, K., Schwaighofer, A., Tresp, V., Xu, X., and Kriegel, H.-P. 2004. Probabilistic memory-based collaborative filtering. IEEE Trans. Knowl. Data Eng. 16, 1, 56--69. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Zhang, L.-J., Zhang, J., and Cai, H. 2007. Services Computing. Springer and Tsinghua University Press.Google ScholarGoogle Scholar
  54. Zheng, Z. and Lyu, M. R. 2010. Collaborative reliability prediction for service-oriented systems. In Proceedings of the IEEE/ACM 32nd International Conference on Software Engineering (ICSE’10). 35--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Zheng, Z., Ma, H., Lyu, M. R., and King, I. 2009. WSREC: A collaborative filtering based web service recommender system. In Proceedings of the 7th International Conference on Web Services (ICWS’09). 437--444. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Zheng, Z., Ma, H., Lyu, M. R., and King, I. 2011. Qos-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4, 2, 140--152. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Transactions on Software Engineering and Methodology
        ACM Transactions on Software Engineering and Methodology  Volume 22, Issue 2
        March 2013
        190 pages
        ISSN:1049-331X
        EISSN:1557-7392
        DOI:10.1145/2430545
        Issue’s Table of Contents

        Copyright © 2013 ACM

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        Publication History

        • Published: 1 March 2013
        • Revised: 1 January 2012
        • Accepted: 1 January 2012
        • Received: 1 April 2011
        Published in tosem Volume 22, Issue 2

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