Skip to main content
SpringerLink
Log in

Forecasting Software Reliability

  • Chapter
Bayesian Methods in Reliability

Part of the book series: Topics in Safety, Reliability and Quality ((TSRX,volume 1))

Abstract

Computer software fails because of the presence of intellectual faults, ranging from simple coding faults to fundamental design faults. In principle, such faults can be permanently removed when they are detected by failure of the software. Then the software will exhibit reliability growth. The problem considered here is the one of forecasting this growth: it includes the estimation of the current reliability of the program from the previous failure data. We begin with a brief description of the software failure process: a non-stationary stochastic process. Several of the best-known software reliability growth models are described, and examples given of their performance on real software failure data. They shown marked disagreement and thus reveal a need for methods of comparing and evaluating software reliability forecasts. Several simple techniques for conducting this evaluation are described and illustrated using several different models on real data sets. Finally, it is shown how in certain circumstances it is possible to improve the predictive accuracy of software reliability models by a re-calibration technique.

This chapter has been published before, see Littlewood, B (1989), Forecasting Software Reliability, Lecture Notes in Computer Science, No. 341, Springer-Verlag, Berlin.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Abdel Chaly, A.A. (1986), Ph D Thesis, City University, London.

    Google Scholar 

  • Adams, E.N. (1984), Optimizing preventive service of software products, IBM Journal of Research and Development, 28 (No 1).

    Google Scholar 

  • Aitchison, J. & Dunsmore, I.R. (1975), Statistical Prediction Analysis, Cambridge University Press, Cambridge.

    Book  MATH  Google Scholar 

  • Akaike, H. (1982), Prediction and Entropy, MRC Technical Summary Report, Mathematics Research Center, University of Wisconsin-Madison.

    Google Scholar 

  • Amman, P.E. & Knight, J.C. (1987), Data diversity: an approach to software fault tolerance, Digest FTCS-17 (17th International Symposium on Fault-Tolerant Computing), 122–126.

    Google Scholar 

  • Ascher, H. & Feingold, H. (1984), Repairable Systems Reliability, Marcel Dekker, New York.

    MATH  Google Scholar 

  • Braun, H. & Paine, J.M. (1977), A comparative study of models for reliability growth, Technical Report No 126, Series 2, Department of Statistics, Princeton University.

    Google Scholar 

  • Brocklehurst, S. (1987), On the effectiveness of adaptive software reliability modelling, CSR Technical Report, City University, London.

    Google Scholar 

  • Chan, P.Y., Littlewood, B. & Snell, J. (1985), Parametric spline approach to adaptive reliability modelling, CSR Technical Report, City University, London.

    Google Scholar 

  • Cox, D.R. & Lewis, P.A.W. (1966), The Statistical Analysis of Series of Events, Methuen, London.

    MATH  Google Scholar 

  • Crow, L.H. (1977), Confidence interval procedures for reliability growth analysis, Technical Report No197, US Army Material Systems Analysis Activity, Aberdeen, Md.

    Google Scholar 

  • Dale, C.J. (1982), Software Reliability Evaluation Methods, British Aerospace Dynamics Group, ST-26750.

    Google Scholar 

  • Dawid, A.P. (1982), The well-calibrated Bayesian, (with discussion), Journal of the American Statistical Association, 77, 605–613.

    Article  MathSciNet  MATH  Google Scholar 

  • Dawid, A.P. (1984a), Calibration-based empirical probability, Research Report36, Department of Statistical Science, University College, London.

    Google Scholar 

  • Dawid, A.P. (1984b), Statistical theory: the prequential approach, Journal of the Royal Statistical Society, A, 147, 278–292.

    Article  MathSciNet  MATH  Google Scholar 

  • Dawid, A.P. (1989), Probability Forecasting. In: S. Kotz, N. L. Johnson and C. B. Read (Eds), Encyclopedia of Statistical Sciences, Vol 6, Wiley, New York.

    Google Scholar 

  • Duane, J.T. (1964), Learning curve approach to reliability monitoring, IEEE Transactions on Aerospace, 2, 563–566.

    Article  Google Scholar 

  • Forman, E.H. & Singpurwalla, N.D. (1977), An empirical stopping rule for debugging and testing computer software, Journal of the American Statistical Association, 72, 750–757.

    Google Scholar 

  • Coel, A.L. & Okumoto, K. (1979), Time-dependent error-detection rate model for software reliability and other performance measures, IEEE Transactions on Reliability, 28, 206–211.

    Article  Google Scholar 

  • Goudie, I. (1984), private communication.

    Google Scholar 

  • Jelinski, Z. & Moranda, P.B. (1972), Software reliability research. In:W. Freiberger (Ed), Statistical Computer Performance Evaluation, Academic Press, New York, 465–484.

    Google Scholar 

  • Joe, H. & Reid, N. (1985), Estimating the number of faults in a system, Journal of the American Statistical Association, 80, 222–226.

    Article  MathSciNet  Google Scholar 

  • Keiller, P.A., Littlewood, B., Miller, D.R. & Sofer, A. (1983a), On the quality of software reliability predictions, Proc. NATO ASI on Electronic Systems Effectiveness and Life Cycle Costing (Norwich, UK, 1982 ), Springer, Berlin, 441–460.

    Google Scholar 

  • Keiller, P.A., Littlewood, B., Miller, D.R. & Sofer, A. (1983b), Comparison of software reliability predictions, Digest FTCS 13 (13th International Symposium on Fault-Tolerant Computing), 128–134.

    Google Scholar 

  • Kendall, M.G. & Stuart, A. (1961), The Advanced Theory of Statistics, Griffin, London.

    Google Scholar 

  • Langberg, N. & Singpurwalla, N.D. (1981), A unification of some software reliability models via the Bayesian approach, Technical Report, TM-66571, The George Washington University, Washington DC.

    Google Scholar 

  • Laprie, J.C. (1984), Dependability evaluation of software systems in operation, IEEE Transactions on Software Engineering, 10.

    Google Scholar 

  • Littlewood, B. (1979), How to measure software reliability and how not to, IEEE Transactions on Reliability, 28, 103–110.

    Article  Google Scholar 

  • Littlewood, B. (1981), Stochastic reliability growth: a model for fault-removal in computer programs and hardware designs, IEEE Transactions on Reliability, 30, 313–320.

    Article  Google Scholar 

  • Littlewood, B. & Keiller, P.A. (1984), Adaptive software reliability modelling, Digest FTCS-14 (14th International Conference on Fault-Tolerant Computing), 108–113.

    Google Scholar 

  • Littlewoon, B. & Sofer, A. (1985), A Bayesian modification to the Jelinski-Moranda software reliability growth model, CSR Technical Report.

    Google Scholar 

  • Littlewood, B. & Verrall, J.L. (1973), A Bayesian reliability growth model for computer software, (Applied Statistics), 22, 332–346.

    Article  MathSciNet  Google Scholar 

  • littlewood, B. & Verrall, J.L. (1981), On the likelihood function of a debugging model for computer software reliability, IEEE Transactions on Reliability, 30, 145–148.

    Article  MATH  Google Scholar 

  • Miller, D.R. (1983), private communication.

    Google Scholar 

  • Miller, D.R. (1986), Exponential order statistic models of software reliability growth, IEEE Transactions on Software Engineering, 12, 12–24.

    Article  MATH  Google Scholar 

  • Musa, J.D. (1975), A theory of software reliability and its application, IEEE Transactions on Software Engineering, 1, 312–327.

    Article  Google Scholar 

  • Musa, J.D. (1979), Software reliability data, report available from Data and Analysis Center for Software, Rome Air Development Center, Rome, NY.

    Google Scholar 

  • Nagel, P.M. & Skrivan, J.A. (1981), Software reliability: repetitive run experimentation and modelling, BCS-40399(Dec.), Boeing Computer Services Company, Seattle, Washington.

    Google Scholar 

  • Rosenblatt, M. (1952), Remarks on a multivariate transformation, Annals of Mathematical Statistics, 23, 470–472.

    Article  MathSciNet  MATH  Google Scholar 

  • Shooman, M. (1973), Operational testing and software reliability during program development, in Record. 1973 IEEE Symp. Computer Software Reliability (New York, 1973, April 30-May 2), 51–57.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Software Reliability, City University, Northampton Square, London, EC1V 0HB, UK

    Bev Littlewood

Authors
  1. Bev Littlewood
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. TU Eindhoven, The Netherlands

    P. Sander  & R. Badoux  & 

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Littlewood, B. (1991). Forecasting Software Reliability. In: Sander, P., Badoux, R. (eds) Bayesian Methods in Reliability. Topics in Safety, Reliability and Quality, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3482-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-3482-8_6

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-5539-0

  • Online ISBN: 978-94-011-3482-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation