Research Article | Open Access
Software Failure Probability Quantification for System Risk Assessment
Risk caused by safety-critical I&C systems considerably affects overall plant risk. Software failures in digitalized I&C systems must be considered as the cause of risk. As digitalization of safety-critical systems progresses, the need for software failure probability quantification increases. For the software of safety-critical systems, very high reliability is required. This article aims at providing an overview of promising software failure probability quantification models for this kind of safety-critical system: The software reliability growth model (SRGM), the input-domain-based test model (IDBT), and the validation/verification quality model (VVQM). In order to accommodate the characteristics of safety-critical systems, a more effective framework of practical risk assessment applications is necessary. In this article, we propose the combined use of SRGM&VVQM for a more systematic and traceable method of the failure probability quantification of safety-critical software.
- R. M. White and D. B. Boettcher, “Putting sizewell B digital protection in context,” Nuclear Engineering International, pp. 41–43, 1994.
- T. L. Chu, G. Martinez-Guridi, and M. Yue, “Traditional Probabilistic Risk Assessment Methods for Digital Systems, Brookhaven National Laboratory,” 2008.
- National Research Council, Digital Instrumentation and Control Systems in Nuclear Power Plants, National Academy Press, Washington, DC, USA, 1997.
- HSE, The Use of Computers in Safety-Critical Applications, HSE Books, London, UK, 1998.
- NEA/CSNI/R(97)23, “Operating and maintenance experience with computer-based systems in nuclear power plants,” 1998.
- N. D. Singpurwalla, “The failure rate of software: Does It exist?,” IEEE Transactions on Reliability, vol. 44, no. 3, pp. 463–469, 1995.
- 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.
- 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.
- 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.
- P. H. Seong et al., Reliability and Risk Issues in Large Scale Safety-Critical Digital Control Systems, Springer, Berlin, Germany, 2008.
- J. Musa and A. Ackerman, “Quantifying software validation: when to stop testing,” IEEE Software, vol. 6, no. 3, pp. 19–27, 1989.
- 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.
- 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.
- INL, “Technology roadmap on instrumentation, control, and human-machine interface to support DOE advanced nuclear energy programs,” Idaho National Laboratory, March 2007.
- 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.
- B. Littlewood and L. Strigini, “Validation of ultrahigh dependability for software-based systems,” Communication of the ACM, vol. 36, no. 11, 1993.
- 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.
- Regulatory Guide 1.152, “Criteria for use of computers in safety systems of nuclear power plants,” Rev. 2, USNRC, 2006.
- 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.
- M. E. Fagan, “Design and code inspections to reduce errors in program development,” IBM Systems Journal, vol. 15, no. 3, 1976.
- G. Dahll, “The use of Bayesian belief nets in safety assessment of software based system,” HWP-527, Halden Project, 1998.
- 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.
- 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.
- IEEE, “IEEE standard criteria for digital computers in safety systems of nuclear power generating stations,” IEEE-126.96.36.199, 2003.
- D. Kahneman, P. Slovic, and A. Tversky, Judgment under Uncertainty: Heuristics and Biases, Cambridge University Press, Cambridge, UK, 1982.
- L. Uusitalo, “Advantages and challenges of Bayesian networks in environmental modeling,” Ecological Modeling, vol. 203, pp. 312–318, 2007.
- 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.
- 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.
- J. D. Musa, A. Iannino, and K. Okumoto, Software Reliability: Measurement, Prediction, Application, McGraw-Hill, New York, NY, USA, 1987.
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.