Table of Contents
Scholarly Research Exchange
Volume 2009, Article ID 163456, 9 pages
Research Article

Software Failure Probability Quantification for System Risk Assessment

1Korea Atomic Energy Research Institute, Daejeon 305-600, South Korea
2Joongbu University, Chungnam 312-702, South Korea

Received 9 June 2009; Accepted 19 August 2009

Copyright © 2009 Hyun Gook Kang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. M. White and D. B. Boettcher, “Putting sizewell B digital protection in context,” Nuclear Engineering International, pp. 41–43, 1994. View at Google Scholar
  2. T. L. Chu, G. Martinez-Guridi, and M. Yue, “Traditional Probabilistic Risk Assessment Methods for Digital Systems, Brookhaven National Laboratory,” 2008. View at Google Scholar
  3. National Research Council, Digital Instrumentation and Control Systems in Nuclear Power Plants, National Academy Press, Washington, DC, USA, 1997.
  4. HSE, The Use of Computers in Safety-Critical Applications, HSE Books, London, UK, 1998.
  5. NEA/CSNI/R(97)23, “Operating and maintenance experience with computer-based systems in nuclear power plants,” 1998. View at Google Scholar
  6. N. D. Singpurwalla, “The failure rate of software: Does It exist?,” IEEE Transactions on Reliability, vol. 44, no. 3, pp. 463–469, 1995. View at Publisher · View at Google Scholar
  7. H. G. Kang, M. C. Kim, S. J. Lee et al., “An overview of risk quantification issues for digitalized nuclear power plants using a static fault tree,” Nuclear Engineering and Technology, vol. 41, no. 6, pp. 849–858, 2009. View at Google Scholar
  8. C. A. Asad, M. I. Ullah, and M. J. Rehman, “An approach for software reliability model selection,” in Proceedings of the 28th Annual International Computer Software and Applications Conference (COMSAC '04), vol. 1, pp. 534–539, IEEE, Hong Kong, September 2004.
  9. A. Wood, “Software reliability growth models: assumptions vs. reality,” in Proceedings of the International Symposium on Software Reliability Engineering (ISSRE '97), pp. 136–141, 1997.
  10. P. H. Seong et al., Reliability and Risk Issues in Large Scale Safety-Critical Digital Control Systems, Springer, Berlin, Germany, 2008.
  11. J. Musa and A. Ackerman, “Quantifying software validation: when to stop testing,” IEEE Software, vol. 6, no. 3, pp. 19–27, 1989. View at Publisher · View at Google Scholar
  12. S. D. Sohn and P. H. Seong, “Testing digital safety system software with a testability measure based on a software fault tree,” Reliability Engineering and System Safety, vol. 91, no. 1, pp. 44–52, 2006. View at Publisher · View at Google Scholar
  13. H. G. Kang, H. G. Lim, H. J. Lee, M. C. Kim, and S. C. Jang, “Input-profile-based software failure probability quantification for safety signal generation systems,” Reliability Engineering and System Safety, vol. 94, no. 10, pp. 1542–1546, 2009. View at Google Scholar
  14. INL, “Technology roadmap on instrumentation, control, and human-machine interface to support DOE advanced nuclear energy programs,” Idaho National Laboratory, March 2007. View at Google Scholar
  15. M. H. Chen, M. R. Lyu, and W. E. Wong, “Effect of code coverage on software reliability measurement,” IEEE Transactions on Reliability, vol. 50, no. 2, pp. 165–170, 2001. View at Publisher · View at Google Scholar
  16. B. Littlewood and L. Strigini, “Validation of ultrahigh dependability for software-based systems,” Communication of the ACM, vol. 36, no. 11, 1993. View at Google Scholar
  17. R. W. Butler and G. B. Finelli, “The infeasibility of quantifying the reliability of life-critical real-time software,” IEEE Transactions on Software Engineering, vol. 19, no. 1, 1993. View at Google Scholar
  18. Regulatory Guide 1.152, “Criteria for use of computers in safety systems of nuclear power plants,” Rev. 2, USNRC, 2006.
  19. G. Y. Park and K. C. Kwon, “Software verification & validation for digital reactor protection system,” in Proceedings of the Information and Control Symposium, pp. 190–192, April 2005.
  20. M. E. Fagan, “Design and code inspections to reduce errors in program development,” IBM Systems Journal, vol. 15, no. 3, 1976. View at Google Scholar
  21. G. Dahll, “The use of Bayesian belief nets in safety assessment of software based system,” HWP-527, Halden Project, 1998.
  22. H. S. Eom et al., “Survey of Bayesian belief nets for quantitative reliability assessment of safety critical software used in nuclear power plants,” Korea Atomic Energy Research Institute, 2001. View at Google Scholar
  23. N. E. Fenton, M. Neil, and D. Marquez, “Using Bayesian networks to predict software defects and reliability,” Journal of Risk and Reliability, vol. 222, no. 4, pp. 701–712, 2008. View at Publisher · View at Google Scholar
  24. IEEE, “IEEE standard criteria for digital computers in safety systems of nuclear power generating stations,” IEEE-, 2003.
  25. D. Kahneman, P. Slovic, and A. Tversky, Judgment under Uncertainty: Heuristics and Biases, Cambridge University Press, Cambridge, UK, 1982.
  26. L. Uusitalo, “Advantages and challenges of Bayesian networks in environmental modeling,” Ecological Modeling, vol. 203, pp. 312–318, 2007. View at Google Scholar
  27. H. S. Son, H. G. Kang, and S. C. Chang, “Procedure for application of software reliability growth models to NPP PSA,” Nuclear Engineering and Technology, vol. 41, no. 8, pp. 1065–1072, 2009. View at Google Scholar
  28. D. R. Prince Williams, “Prediction capability analysis of two and three parameters software reliability growth models,” Information Technology Journal, vol. 5, no. 6, pp. 1048–1052, 2006. View at Google Scholar
  29. J. D. Musa, A. Iannino, and K. Okumoto, Software Reliability: Measurement, Prediction, Application, McGraw-Hill, New York, NY, USA, 1987.