Establishing the Correlation between Complexity and a Reliability Metric for Software Digital I&C-Systems

  • John Eidar Simensen
  • Christian Gerst
  • Bjørn Axel Gran
  • Josef Märtz
  • Horst Miedl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5775)


Faults introduced in design or during implementation might be prevented by design validation and by evaluation during implementation. There are numerous methods available for validating and evaluating software. Expert judgment is a much used approach to identify problematic areas in design or target challenges related to implementation. ISTec and IFE cooperate on a project on automated complexity measurements of software of digital instrumentation and control (I&C) systems. Metrics measured from the function blocks and logic diagrams specifying I&C-systems are used as input to a Bayesian Belief Net describing correlation between inputs and a complexity metric. By applying expert judgment in the algorithms for the automatic complexity evaluation, expert judgment is applied to entire software systems. The results from this approach can be used to identify parts of software which from a complexity viewpoint is eligible for closer inspection. In this paper we describe the approach in detail as well as plans for testing the approach.


Intermediate Node Complexity Measurement Expert Judgment Input Node Function Block 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fenton, N., Littlewood, B., Neil, M., Strigini, L., Sutcliffe, A., Wright, D.: Assessing Dependability of Safety Critical Systems using Diverse Evidence. In: IEEE Proceedings Software Engineering, vol. 145(1), pp. 35–39 (1998)Google Scholar
  2. 2.
    Dahll, G.: Safety Assessment of Software Based Systems. In: SAFECOMP - The International Conference on Computer Safety, Reliability and Security, pp. 14–24. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Boehm, B.W.: Software engineering economics. IEEE Transitional on Software Engineering 10(1), 7–19 (1984)Google Scholar
  4. 4.
    Jensen, F.: An Introduction to Bayesian Network. UCL Press, University College London (1996)Google Scholar
  5. 5.
    Gran, B.A., Dahll, G., Eisinger, S., Lund, E.J., Norstrm, J.G., Strocka, P., Ystanes, B.J.: Estimating Dependability of Programmable Systems Using BBNs. In: Koornneef, F., van der Meulen, M.J.P. (eds.) SAFECOMP 2000. LNCS, vol. 1943, pp. 309–320. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Gran, B.A.: The use of Bayesian Belief Networks for combining disparate sources of information in the safety assessment of software based systems. Thesis 2002:35, NTNU, Trondheim, Norway (2002)Google Scholar
  7. 7.
    Gran, B.A.: Use of Bayesian Belief Networks when Combining Disparate Sources of Information in the Safety Assessment of Software Based Systems. International Journal of Systems Science 33(6), 529–542 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Gran, B.A., Helminen, A.: A Bayesian Belief Network for Reliability Assessment. In: Voges, U. (ed.) SAFECOMP 2001. LNCS, vol. 2187, pp. 35–45. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Bishop, P.G., Esp, D.G., Barnes, M., Humphreys, P., Dahll, G.: PODSA project on diverse software. IEEE Trans. Softw. Eng. 12(9), 929–940 (1986)CrossRefGoogle Scholar
  10. 10.
    Märtz, J., Lindner, A., Miedl, H.: Complexity Measurement of Software in Digital I&C-Systems. In: Sixth American Nuclear Society Int. Topic Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies. NPIC&HMIT 2009, Knoxville Tennessee (2009)m American Nuclear Society, LaGrange Park (2009)Google Scholar
  11. 11.
    Lyu, M.R.: Handbook of Software Reliability Engineering, pp. 699–705. IEEE Computer Society Press, McGraw-Hill (1996)Google Scholar
  12. 12.
    Lyu, M.R.: Handbook of Software Reliability Engineering, pp. 41–43. IEEE Computer Society Press, McGraw-Hill (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • John Eidar Simensen
    • 1
  • Christian Gerst
    • 2
  • Bjørn Axel Gran
    • 1
  • Josef Märtz
    • 2
  • Horst Miedl
    • 2
  1. 1.Institute for energy technologyHaldenNorway
  2. 2.Institute for Safety Technology GmbHGarching near MunchenGermany

Personalised recommendations