Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

A Bayesian modification to the Jelinski-Moranda software reliability growth model

A Bayesian modification to the Jelinski-Moranda software reliability growth model

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Software Engineering Journal — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Electrical Engineers
Published

The Jelinski-Moranda (JM) model for software reliability growth is one of the most commonly cited (often in its guise as the ‘Musa model’). Recent studies show that the reliability estimates and predictions given by the model are often grossly inaccurate. It has been suggested that one reason for this poor performance may be the use of the maximum-likelihood method of inference. This paper describes a Bayesian version of the model and shows that it is sometimes an improvement on JM. However, both versions have a tendency to give optimistic answers, probably owing to a key, but implausible, underlying assumption common to both models. The authors conclude that the generally poor performance of the models is such that they should only be used with great caution.

Inspec keywords: programming theory; Bayes methods; software reliability

Other keywords: Musa model; Jelinski-Moranda software reliability growth model; Bayesian modification

Subjects: Programming and algorithm theory; Other topics in statistics; Systems analysis and programming

References

    1. 1)
      • H. Joe , N. Reid . Estimating the number of faults in a system. Journal of american statistical association , 222 - 226
    2. 2)
      • B. Littlewood . Theories of software reliability: how good are they and how can they be improved?. IEEE transaction on software engineering , 489 - 500
    3. 3)
      • A.P. Dawid . Statistical theory: the prequential approach. Journal of royal statistical society series A , 278 - 292
    4. 4)
      • Shooman, M.: `Operational testing and software reliability during program development', Record of 1973 IEEE Symposium on Computer Software Reliability, April/May 1973, New York, USA, p. 51–57.
    5. 5)
      • J.D. Musa . A theory of software reliability and its application. IEEE transactions on software engineering , 5 , 312 - 327
    6. 6)
      • B. Littlewood , J.L. Verrall . A bayesian reliability growth model for computer software. Journal of royal statistical society series C (applied statistics) , 332 - 346
    7. 7)
      • B. Littlewood . Stochastic reliability growth: a model for fault-removal in computer programs and hardware designs. IEEE transactions on reliability , 313 - 320
    8. 8)
      • B. Littlewood , J.L. Verrall . On the likelihood function of a debugging model for computer software reliability. IEEE transactions on reliability , 145 - 148
    9. 9)
      • B. Littlewood . How to measure software reliability and how not to. IEEE transactions on reliability , 103 - 110
    10. 10)
      • A.A. Abdel Ghaly , P.Y. Chan , B. Littlewood . Evaluation of competing software reliability predictions. IEEE transactions on software engineering , 9 , 950 - 967
    11. 11)
      • E.N. Adams . Optimizing preventive service of software products. IBM journal of research & development , 1
    12. 12)
      • Keiller, P.A., Littlewood, B., Miller, D.R., Sofer, A.: `On the quality of software reliability prediction', Proceedings of NATO Advanced Study Institute: Electronic Systems Effectiveness and Life Cycle Costing, 1983, Springer-Verlag, p. 441–445.
    13. 13)
      • Musa, J.D.: `Software reliability data', Report Available from Data & Analysis Center for Software, .
    14. 14)
      • Z. Jelinski , P.B. Moranda , W. Freiberger . (1972) Software reliability research, Statistical computer performance evaluation.
    15. 15)
      • M. Abramowitz , I. Stegun . (1964) , Handbook of mathematical functions.
    16. 16)
      • D.R. Cox , P.A.W. Lewis . (1966) , Statistical analysis of series of events.
    17. 17)
      • A.K. Goel . Software error detection model with applications. Journal of systems & software , 243 - 249
    18. 18)
      • Goel, A.K., Okumoto, K.: `Bayesian software prediction models. vol. 1: an imperfect debugging model for reliability and other quantitative measures of software systems', RADC-TR-78-155, 1978, NY, USA, Rome Air Development Center.
    19. 19)
      • P.M. Nagel , J.A. Skrivan . (1981) , Software reliability: repetitive run experimentation and modelling.
    20. 20)
      • Braun, H., Schenker, N.: `New models for reliability growth', 174, Technical Report, October 1980.
    21. 21)
      • Forman, E.H.: `Statistical models and methods for measuring software reliability', 1974, D.Sc. Dissertation, George Washington University, Washington, DC, USA, SEAS.
    22. 22)
      • E.H. Forman , N.D. Singpurwalla . An empirical stopping rule for debugging and testing computer software. Journal of american atatistical association , 750 - 757
    23. 23)
      • M. Shooman , W. Freiberger . (1972) Probabilistic models for software reliability and prediction, Statistical computer performance evaluation.
    24. 24)
      • Crow, L.H.: `Confidence interval procedures for reliability growth analysis', 197, Technical Report, 1977.
    25. 25)
      • A. Iannino , J.D. Musa , K. Okumoto , B. Littlewood . Criteria for software reliability model comparisons. IEEE transactions on software engineering , 6 , 687 - 691
    26. 26)
      • R.J. Meinhold , N.D. Singpurwalla . Bayesian analysis of a commonly used model for describing software failures. The statistician , 168 - 173
    27. 27)
      • M.H. DEGROOT . (1970) , Optimal statistical decisions.
    28. 28)
      • Littlewood, B.: `What makes a reliable program: few bugs or a small failure rate?', Proceedings of 1980 National Computer Conference, 1980, Arlington, VA, AFIPS Press, p. 707–713.
    29. 29)
      • Braun, H., Paine, J.M.: `A comparative study of models for reliability growth', 126, Technical Report, July 1977, Series 2.
http://iet.metastore.ingenta.com/content/journals/10.1049/sej.1987.0005
Loading

Related content

content/journals/10.1049/sej.1987.0005
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address