Multistage Model for Residual Fault Prediction

  • Ajeet Kumar Pandey
  • Neeraj Kumar Goyal
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 303)


Software reliability is defined as the probability of failure-free software operation for a specified period of time in a specified environment and is widely recognized as one of the most significant attributes of software quality (Lyu 1996). Over past decades, many software reliability growth models (SRGMs) have been presented to estimate important reliability measures such as the mean time to failure, the number of remaining faults, defect levels, and the failure intensity. Software reliability can be viewed form the two view points—user’s view and developer’s view. From a user’s point of view, software reliability can be defined as the probability of a software system or component to perform its intended function under the specified operating conditions over the specified period of time. From developer’s point of view, the reliability of the system can be measured as the number of residual faults that are likely to be found during testing or operational usage. This study aims to assure software reliability from developer’s point of view.


Fuzzy Number Fuzzy Inference System Mean Absolute Percent Error Software Reliability Fault Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Fenton, N. E., & Neil, M. (1999). A critique of software defect prediction models. IEEE Transaction on Software Engineering, 25(5), 675–689.CrossRefGoogle Scholar
  2. Fenton, N. E., & Neil, M. (2000), Software metrics: Roadmap, Proceedings of the Conference on the Future of Software Engineering, (pp. 375–370). Limerick, Ireland.Google Scholar
  3. Fenton, N., Neil, N., Marsh, W., Hearty, P., Radlinski, L., & Krause, P. (2008). On the effectiveness of early life cycle defect prediction with Bayesian nets. Empirical of Software Engineering, 13, 499–537.CrossRefGoogle Scholar
  4. Goel, A. L. (1985). Software reliability models: Assumptions, limitations, and applicability. IEEE Transaction on Software Engineering, SE11(12), 1411–1423.Google Scholar
  5. Goel, A. L., & Okumoto, K. (1979). A Time-dependent error detection rate model for software reliability and other performance measure. IEEE Transaction on Reliability, R-28, 206–211.Google Scholar
  6. IEEE (1991). IEEE standard glossary of software engineering terminology, STD-729-991, ANSI/IEEE.Google Scholar
  7. Kan, S. H. (2002). Metrics and models in software quality engineering (2nd ed.). Reading, MA: Addison Wesley.Google Scholar
  8. Khoshgoftaar, T. M., & Munson, J. C. (1990). Predicting software development errors using complexity metrics. IEEE Journal on Selected Areas in Communication, 8(2), 253–261.CrossRefGoogle Scholar
  9. Kumar, K. S., & Misra, R. B. (2008). An enhanced model for early software reliability prediction using software engineering metrics, Proceedings of 2nd International Conference on Secure System Integration and Reliability Improvement, (pp. 177–178).Google Scholar
  10. Li, M., & Smidts, C. (2003). A ranking of software engineering measures based on expert opinion. IEEE Transaction on Software Engineering, 29(9), 811–824.CrossRefGoogle Scholar
  11. Lyu, M. R. (1996). Handbook of Software Reliability Engineering. NY: McGraw–Hill/IEE Computer Society Press.Google Scholar
  12. Mamdani, E. H. (1977). Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transaction on Computers, 26(12), 1182–1191.MATHCrossRefGoogle Scholar
  13. NASA (2004), NASA metrics data program,
  14. Pandey, A. K., & Goyal, N. K. (2010). Multistage fault prediction model using process level software metrics. International Journal of Communications in Dependability and Quality Management, 13(1), 54–66.Google Scholar
  15. Paulk, M. C., Weber, C. V., Curtis, B., & Chrissis, M. B. (1993). Capability maturity model version 1.1. IEEE Software, 10(3), 18–27.CrossRefGoogle Scholar
  16. Pressman, R. S. (2005). Software engineering: A practitioner’s approach (6th ed.). New York: McGraw-Hill Publication.Google Scholar
  17. PROMISE repository (2007).
  18. Ross, T. J. (2005). Fuzzy logic with engineering applications (2nd ed.). India: Wiley.Google Scholar
  19. Schneidewind, N. F. (1992). Methodology for validating software metrics. IEEE Transactions on Software Engineering, 18(5), 410–422.CrossRefGoogle Scholar
  20. Zhang, X., & Pham, H. (2000). An analysis of factors affecting software reliability. The Journal of Systems and Software, 50(1), 43–56.CrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Engineering and Manufacturing ServicesCognizant Technology SolutionHyderabadIndia
  2. 2.Reliability Engineering CentreIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations