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Reseach Article

Modified Jelinski-Moranda Software Reliability Model with Imperfect Debugging Phenomenon

by G. S. Mahapatra, P. Roy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 18
Year of Publication: 2012
Authors: G. S. Mahapatra, P. Roy

G. S. Mahapatra, P. Roy . Modified Jelinski-Moranda Software Reliability Model with Imperfect Debugging Phenomenon. International Journal of Computer Applications. 48, 18 ( June 2012), 38-46. DOI=10.5120/7451-0534

@article{ 10.5120/7451-0534,
author = { G. S. Mahapatra, P. Roy },
title = { Modified Jelinski-Moranda Software Reliability Model with Imperfect Debugging Phenomenon },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 18 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-46 },
numpages = {9},
url = { },
doi = { 10.5120/7451-0534 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:44:27.274477+05:30
%A G. S. Mahapatra
%A P. Roy
%T Modified Jelinski-Moranda Software Reliability Model with Imperfect Debugging Phenomenon
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 18
%P 38-46
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

In this paper, we have modified the Jelinski-Moranda (J-M) model of software reliability using imperfect debugging process in fault removal activity. The J-M model was developed assuming the debugging process to be perfect which implies that there is one-to-one correspondence between the number of failures observed and faults removed. But in reality, it is possible that the fault which is supposed to have been removed may cause a new failure. In the proposed modified J-M model, we consider that whenever a failure occurs, the detected fault is not perfectly removed and there is a chance of raising new fault/faults due to wrong diagnosis or incorrect modifications in the software. In this paper, we develop a modified J-M model which can describe the imperfect debugging process. The parameters of our modified J-M model are estimated by using maximum-likelihood estimation method. Applicability of the model has been shown on the failure data set of Musa.

  1. Lyu, M. R. 1996. Handbook of Software Reliability Engineering. McGraw-Hill.
  2. Musa, J. D. , Iannino, A. , and Okumoto, K. 1990. Software Reliability: Measurement, Prediction, Application. McGraw-Hill.
  3. Jelinski, Z. and Moranda, P. B. 1972. Software reliability research, Statistical Computer Performance Evaluation. Academic Press: New York, 465-484.
  4. Littlewood, B. 1987. How good are software reliability predictions?. Software Reliability: Achievement and Assessment. Blackwell Scientific Publications. 154-166.
  5. Musa J. D. 1975. A theory of software reliability and its application. IEEE T. Software Eng. 1(3), 312-327.
  6. Goel, A. L. , and Okumoto, K. 1979. Time dependent error detection rate model for software reliability and other performance measures. IEEE T. Reliab. R-28(3), 206-211.
  7. Yamada, S. , Ohba, M. and Osaki, S. 1983. S-shaped reliability growth modeling for software error detection. IEEE T. Reliab. R-32(5), 475-484.
  8. Ohba, M. 1984. Software reliability analysis models. IBM J. Res. Dev. 28(4), 428-443.
  9. Goel, A. L. 1985. Software reliability models: assumptions, limitations and applicability. IEEE T. Software Eng. SE-11(12), 1411-1423.
  10. Kapur, P. K. , and Garg, R. B. 1990. Optimal release policy for software reliability growth models under imperfect debugging. Oper. Res. RAIRO. 24(3), 295-305.
  11. Chang, Y. C. , and Liu, C. T. 2009. A generalized JM model with applications to imperfect debugging in software reliability. Appl. Math. Model. 33, 3578-3588.
  12. Shyur, H. J. 2003. A stochastic software reliability model with imperfect-debugging and change-point. J. Syst. Software. 66(2), 135-141.
  13. Kapur, P. K. , Singh, O. M. P. , Shatnawi, O. , and Gupta, A. 2006. A discrete NHPP model for software reliability growth with imperfect fault debugging and fault generation. Int. J. Perform. Eng. 2(4), 351-368.
  14. Prasad, R. S. , Raju, O. N. , and Kantam, R. R. L. 2010. SRGM with imperfect debugging by genetic algorithms. Int. J. Software Eng. Appl. 1(2), 66-79.
  15. Raju, O. N. 2011. Software reliability growth models for the safety critical software with imperfect debugging. Int. J. Comput. Sci. Eng. 3(8), 3019-3026.
  16. Xie, M. Dai, Y. S. and Poh, K. L. 2004. Computing System Reliability Models and Analysis. Kluwer Academic Publisher.
  17. Kremer, W. 1983. Birth-death and bug counting. IEEE T. Reliab. R-32(1), 37-47.
  18. Musa, J. D. 1980. Software Reliability Data. Data & Analysis Center for Software.
  19. Dawid, A. P. 1984. Statistical theory: the prequential approach. J. Roy. Stat. Soc. A. 147, 278-292.
  20. Pham, H. 2006. System Software Reliability. Springer.
  21. Bittanti, S. 1988. Software Reliability Modelling and Identification (Lecture Notes in Computer Science). Springer-Verlag.
Index Terms

Computer Science
Information Sciences


Software Reliability Jelinski-moranda Model Failure Maximum Likelihood Estimation Imperfect Debugging