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Annals of Computer Science and Information Systems, Volume 14

Proceedings of the 2017 International Conference on Information Technology and Knowledge Management

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Reliability Modeling of OSS Systems based on Innovation-Diffusion Theory and Imperfect Debugging

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DOI: http://dx.doi.org/10.15439/2017KM48

Citation: Proceedings of the 2017 International Conference on Information Technology and Knowledge Management, Ajay Jaiswal, Vijender Kumar Solanki, Zhongyu (Joan) Lu, Nikhil Rajput (eds). ACSIS, Vol. 14, pages 5358 ()

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Abstract. Open Source Software (OSS) has obtained widespread popularity in last few decades due to the exceptional contribution of some well established ones like Apache, Android, MySQL, LibreOffice, Linux etc. not only in the field of information technology but also in other sectors such as research, business and education. These systems are characterized by a huge shift in development pattern they adopt in comparison to proprietary software. Reliability modeling for such systems therefore is a growing area of research now days. Number of users adopting and working on refinement of such systems post-release play an indispensible role in their reliability growth. In this paper, we have proposed a software reliability growth model (SRGM) based on Non-homogeneous Poisson process (NHPP) based on number of users, under the phenomenon of Imperfect Debugging. The renowned Bass Model from Marketing based on the Theory of Diffusion of Innovation is used to depict the user growth phenomenon. Various fault content functions are considered in proposed models to represent imperfect debugging conditions and their performance is evaluated on fault dataset of GNOME 2.0. Four goodness-of-fit criteria namely Coefficient of Determination, Mean Square Error, Predictive Ratio Risk, and Predictive Power are used to calculate the estimation accuracy of all the proposed models and it has been observed that prediction capabilities of models based on imperfect debugging phenomenon is better than model assuming perfect debugging situation.

References

  1. W. S. Jawadekar, “Software Engg”, Tata McGraw-Hill Education, 2004.
  2. P. Kapur, H. Pham, A. Gupta, P. Jha, “Software reliability assessment with OR applications”, Springer, 2011.
  3. A. L. Goel, K. Okumoto, “Time-dependent error-detection rate model for software reliability and other performance measures”, IEEE transactions on Reliability, Vol. 28, Issue 3, 1979, 206–211.
  4. S. Yamada, M. Ohba, S. Osaki, “S-shaped reliability growth modeling for software error detection”, IEEE Transactions on reliability Vol. 32, Issue 5, 1983, 475–484.
  5. J.D. Musa, K. Okumoto, “A logarithmic Poisson execution time model for software reliability measurement”. In Proceedings of the 7th international conference on Software engineering, IEEE Press, 1984, 230-238.
  6. J.W. Paulson, G. Succi,A. Eberlein, “An empirical study of open-source and closed-source software products”, IEEE Transactions on Software Engineering, Vol. 30, Issue 4,2004,246-256.
  7. B. Rossi, B. Russo, G. Succi, “Modelling failures occurrences of open source software with reliability growth”, Open Source Software: New Horizons, 2010, 268–280.
  8. S. Yamada, M. Yamaguchi, “A method of statistical process control for successful open source software projects and its application to determining the development period , International Journal of Reliability, Quality and Engineering, Vol. 23, Issue 5, 2016.
  9. X. Li, Y. F. Li, M. Xie, S. H. Ng, “Reliability analysis and optimal version-updating for open source software”, Information and Software Technology, Vol. 53, Issue 9, 2011, 929–936.
  10. Y. Tamura, S. Yamada, “A component-oriented reliability assessment method for open source software”, International Journal of Reliability, Quality and Safety Engineering, Vol. 15, Issue 1 ,2008, 33-53.
  11. J. Yang, Y. Liu, M. Xie, M. Zhao, “Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes”, Journal of Systems and Software, Vol. 115, 2016, 102–110.
  12. C. Rahmani, A.H. Azadmanesh, L. Najjar, “A Comparative Analysis of Open Source Software Reliability”, JSW, Vol. 5, Issue 12, 2010, 1384-1394.
  13. C. Rahmani, H. Siy, A. Azadmanesh, “An experimental analysis of open source software reliability”, Department of Defense/Air Force Office of Scientific Research, 2009.
  14. Y. Tamura, S. Yamada, “Comparison of software reliability assessment methods for open source software, in: Parallel and Distributed Systems”, 2005. Proceedings. 11th International Conference on, Vol. 2, IEEE, 2005, 488–492.
  15. Y. Zhou, J. Davis, “Open source software reliability model: an empirical approach”, ACM SIGSOFT Software Engineering Notes, Vol. 30, 2005, 1–6.
  16. P. Kapur, H. Pham, S. Anand, K. Yadav, “A unified approach for developing software reliability growth models in the presence of imperfect debugging and error generation”, IEEE Transactions on Reliability Vol. 60, Issue 1, 2011, 331–340.
  17. H. Pham, “A software cost model with imperfect debugging, random life cycle and penalty cost”, International Journal of Systems Science Vol. 27 , Issue 5, 1996, 455–463.
  18. C. T. Lin, “Analyzing the effect of imperfect debugging on software fault detection and correction processes via a simulation framework”, Mathematical and Computer Modeling, Vol. 54, Issue 11, 2011, 3046–3064.
  19. E. M. Rogers, “Diffusion of innovations”, Simon and Schuster, 2010.
  20. F. M. Bass, “A new product growth for model consumer durables”, Management science, Vol. 15, Issue 5, 1969, 215-227.
  21. S. Yamada, K. Tokuno, S. Osaki, “Imperfect debugging models with fault introduction rate for software reliability assessment”, International Journal of Systems Science, Vol. 23, Issue 12, 1992, 2241-2252.
  22. H. Pham, X. Zhang, X,”NHPP software reliability and cost models with testing coverage”, European Journal of Operational Research, Vol. 145, Issue 2, 2003.
  23. N. Gandhi, N. Gondwal, A.G. Aggarwal, A. Tandon, “Estimating Reliability for OSS: An approach with change-point in operational phase”, In proceedings of 6th International Conference on Reliability, Infocom Technologies and Optimization, IEEE, 2017, 251-255.