Advertisement

Efficient reliability growth modelling for industrial software failure data

  • Benedikte Elbel
  • Oliver Maeckel
Conference paper

Abstract

In this paper, we present a pragmatic approach for using stochastic reliability growth models in industrial software quality management. Key concept is the use of efficient algorithms for selecting suitable reliability growth models based on the evaluation of the model’s appropriateness for long term prognoses. Compared to the generally accepted model evaluation criteria U-Plot, Prequential Likelihood and Holdout, the newly developed algorithms reduce speed and allow a selection of the model to be applied with regard to the kind of prognoses it will be used for. The significant increase of speed has shown to be crucial for application in industrial software projects. Our approach has been realised as an efficient, easy to use tool.

Keywords

Correction Process Software Reliability Failure Data Reliability Growth Industrial Software 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liggesmeyer, P. Software Qualität — Testen, Analysieren und Verifizieren von Software. Spektrum Akademischer Verlag, Heidelberg, Berlin, 2002MATHGoogle Scholar
  2. 2.
    Birolini, A. Zuverläsigkeit von Geräten und Systemen. Springer-Verlag, Berlin, Heidelberg, New York 1997CrossRefGoogle Scholar
  3. 3.
    Shooman, M. Operational testing and software reliability during program development. In: IEEE Symp. Comput. Software Rel., New York, 1973, pp. 51-57Google Scholar
  4. 4.
    Jelinski Z, Moranda P. Software reliability research. In: Statistical Computer Performance Evaluation, W. Freiberger (Ed.), New York, Academic, 1972, pp. 465–484Google Scholar
  5. 5.
    Littlewood B, Verall J. A Bayesian reliability growth model for computer software. J. Roy. Statist. Soc., 22, 1973, pp. 332–346Google Scholar
  6. 6.
    Musa J. A theory of software reliability and its application. IEEE Trans Software Eng., vol 1, no 9, 1975, pp.312–327CrossRefGoogle Scholar
  7. 7.
    Keiller P, Littlewood B, Miller D, Sofer A. Comparison of software rel-ability predictions. 3th Int. Symp. Fault-Tolerant Comput, 1983, pp. 128-134Google Scholar
  8. 8.
    Lyu M. Handbook of Software Reliability Engineering. McGraw-Hill, New York, 1995Google Scholar
  9. 9.
    Musa J, Iannino A, Okumoto K. Software Reliability: Measurement, Prediction, Application. McGraw-Hill, New York 1990Google Scholar
  10. 10.
    Liggesmeyer P, Ackermann T. Applying Reliability Engineering: Empir-cal Results, Lessons Learned and Further Improvements. ISSRE 98, Paderborn, November 1998Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Benedikte Elbel
    • 1
  • Oliver Maeckel
    • 1
  1. 1.Siemens AG Corporate TechnologyMunichGermany

Personalised recommendations