Estimating Worst Case Failure Dependency with Partial Knowledge of the Difficulty Function

  • Peter Bishop
  • Lorenzo Strigini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8666)

Abstract

For systems using software diversity, well-established theories show that the expected probability of failure on demand (pfd) for two diverse program versions failing together will generally differ from what it would be if they failed independently. This is explained in terms of a “difficulty function” that varies between demands on the system. This theory gives insight, but no specific prediction unless we have some means to quantify the difficulty function. This paper presents a theory leading to a worst case measure of “average failure dependency” between diverse software, given only partial knowledge of the difficulty function. It also discusses the possibility of estimating the model parameters, with one approach based on an empirical analysis of previous systems implemented as logic networks, to support pre-development estimates of expected gain from diversity. The approach is illustrated using a realistic safety system example.

Keywords

Safety software reliability fault tolerance failure dependency software diversity difficulty function 

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References

  1. 1.
    Bentley, J.G.W., Bishop, P.G., van der Meulen, M.J.P.: An Empirical Exploration of the Difficulty Function. In: Heisel, M., Liggesmeyer, P., Wittmann, S. (eds.) SAFECOMP 2004. LNCS, vol. 3219, pp. 60–71. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Bloomfield, R.E., Guerra, A.S.L.: Process Modelling to Support Dependability Arguments. In: IEEE Dependable Systems and Networks, DSN 2002, pp. 113–122 (2002)Google Scholar
  3. 3.
    Eckhardt, D.E., Caglayan, A.K., et al.: An experimental evaluation of software redundancy as a strategy for improving reliability. IEEE Trans. Software Eng. 17(7), 692–702 (1991)CrossRefGoogle Scholar
  4. 4.
    Eckhardt, D.E., Lee, L.D.: A theoretical basis for the analysis of multiversion software subject to coincident errors. IEEE Transactions on Software Engineering 11(12), 1511–1517 (1985)CrossRefMATHGoogle Scholar
  5. 5.
    Hatton, L.: Reexamining the fault density-component size connection. IEEE Software 14(2), 89–97 (1997)CrossRefGoogle Scholar
  6. 6.
    Hughes, R.P.: A New Approach to Common Cause Failure. Reliability Engineering 17(3), 211–236 (1987)CrossRefGoogle Scholar
  7. 7.
    Knight, J.C., Leveson, N.G.: Experimental evaluation of the assumption of independence in multiversion software. IEEE Trans. Software Engineering 12(1), 96–109 (1986)CrossRefGoogle Scholar
  8. 8.
    Littlewood, B., Miller, D.R.: Conceptual Modelling of Coincident Failures in Multiversion Software. IEEE Transactions on Software Engineering 15(2), 1596–1614 (1989)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Malaiya, Y.K., Denton, J.: Estimating the number of residual defects in software. In: Third IEEE International High-Assurance Systems Engineering Symposium, pp. 98–105. IEEE (1998)Google Scholar
  10. 10.
    Popov, P., et al.: Software diversity as a measure for reducing development risk. In: IEEE Tenth European Dependable Computing Conference, EDCC 2014, pp. 106–117 (2014)Google Scholar
  11. 11.
    Salako, K., Strigini, L.: When does ‘Diversity’ in Development Reduce Common Failures? IEEE Transactions on Dependable and Secure Computing 11(2), 193–206 (2014)CrossRefGoogle Scholar
  12. 12.
    Skiena, S., Revilla, M.: Programming Challenges. Springer (2003) ISBN: 0387001638Google Scholar
  13. 13.
    Sherriff, M., Williams, L.: Defect Density Estimation Through Verification and Validation. In: The 6th Annual High Confidence Software and Systems Conference, Lithicum Heights, MD, pp. 111–117 (2006)Google Scholar
  14. 14.
    Wright, R.I., Pilkington, A.F.: An Investigation into PLC Reliability. HSE Software Reliability Study, GNSR/CI/21. Risk Management Consultants (RMC), Report R94-1(N), Issue B (1995)Google Scholar
  15. 15.
    van der Meulen, M.J.P., Revilla, M.A.: The Effectiveness of Software Diversity in a Large Population of Programs. IEEE Transactions on Software Engineering 34(6), 753–764 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Peter Bishop
    • 1
    • 2
  • Lorenzo Strigini
    • 1
  1. 1.Centre for Software ReliabilityCity UniversityLondonUK
  2. 2.Adelard LLP, LondonLondonUK

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