A Parameter Estimation of Software Reliability Growth Model with Change-Point

변화점을 고려한 소프트웨어 신뢰도 성장모형의 모수추정

  • Published : 2008.10.31


The non-homogeneous Poisson process(NHPP) based software reliability growth models are proved quite successful in practical software reliability engineering. The fault detection rate is usually assumed to be the continuous and monotonic function. However, the fault detection rate can be affected by many factors such as the testing strategy, running environment and resource allocation. This paper describes a parameter estimation of software reliability growth model with change-point problem. We obtain the maximum likelihood estimate(MLE) and least square estimate(LSE), and compare goodness-of-fit.


Software reliability growth model;NHPP;fault-detection rate;mean value function;change-point problem


  1. Pham, H. (1993). Software reliability assessment: Imperfect debugging and multiple failure types in software development, EG&G-RAMM-10737, Idaho National Engineering Laboratory
  2. Shyur, H. J. (2003). A stochastic software reliability model with imperfect-debugging and change-point, The Journal of System and Software, 66, 135-141
  3. Misra, P. N. (1983). Software reliability analysis, IBM Systems of Journal, 22, 465-484
  4. Musa, J. D., Iannino, A. and Okumoto, K. (1987). Software Reliability Measurement Prediction Application, McGraw-Hill, New York
  5. Chang, Y. P. (1997). An analysis of software reliability with change-point models, NSC 85-2121-M031-003, National Science Council, Taiwan
  6. Chang, Y. P. (2001). Estimation of parameters for nonhomogeneous poisson process: Software reliability with change-point model, Communications in Statistics: Simulation and Computation, 30, 623-635
  7. Goel, A. L. and Okumoto, K. (1979). Time-dependent error-detection rate model for software reliability and other performance measures, IEEE Transaction on Reliability, R-28, 206-211
  8. Jelinski, Z. and Moranda, P. B. (1972). Software reliability research, Statistical Computer Performance Evaluation, Freiberger, W. Ed., Academic Press, New York
  9. Yamada, S., Ohba, M. and Osaki, S. (1983). S-shaped reliability growth modeling for software error detection, IEEE Transaction on Reliability, R-32, 475-484
  10. Zhao, M. (1993). Change-point problems in software and hardware reliability, Communications in Statistical-Theory and Mathematics, 22, 757-768
  11. Yamada, S., Tokuno, K. and Osaki, S. (1992). Imperfect debugging models with fault introduction rate for software reliability assessment, International Journal of Systems Science, 23, 2241-2252