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Unification of Software Reliability Models by Self-Exciting Point Processes

Part of: Computer system organization Stochastic processes Special processes

Published online by Cambridge University Press:  01 July 2016

Yiping Chen  and
Nozer D. Singpurwalla
Yiping Chen*
Affiliation:
AT&T Bell Laboratories
Nozer D. Singpurwalla*
Affiliation:
The George Washington University
*
∗ Postal address: AT&T Bell Laboratories, Basking Ridge, New Jersey 07920, USA.
∗∗ Postal address: The George Washington University, Washington, DC 20052, USA.
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Abstract

Assessing the reliability of computer software has been an active area of research in computer science for the past twenty years. To date, well over a hundred probability models for software reliability have been proposed. These models have been motivated by seemingly unrelated arguments and have been the subject of active debate and discussion. In the meantime, the search for an ideal model continues to be pursued. The purpose of this paper is to point out that practically all the proposed models for software reliability are special cases of self-exciting point processes. This perspective unifies the very diverse approaches to modeling reliability growth and provides a common structure under which problems of software reliability can be discussed.

Keywords

POINT PROCESSESRELIABILITY GROWTHSOFTWARE QUALITYSTOCHASTIC PROCESSES

MSC classification

Primary: 68M15: Reliability, testing and fault tolerance
Secondary: 60G55: Point processes 60K10: Applications (reliability, demand theory, etc.)

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 29 , Issue 2 , June 1997 , pp. 337 - 352
Copyright
Copyright © Applied Probability Trust 1997 

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References

Angus, J. E., Schafer, R. E. and Sukert, A. (1980) Software reliability model validation. Proc. 1980 Annual Reliability and Maintainability Symp., pp. 191–2.Google Scholar
Basu, A. P. (1971) Bivariate failure rate. J. Amer. Statist. Assoc. 60, 103–2.Google Scholar
Campdónico, S. and Singpurwalla, N. D. (1994) A Bayesian analysis of the logarithmic-Poisson execution time model based on expert opinion and failure data. IEEE Trans. Software Eng. 20, 677–83.Google Scholar
Chen, Y. and Singpurwalla, N. D. (1994) A non-Gaussian Kalman filter model for tracking software reliability. Statist. Sinica 4, 535–48.Google Scholar
Chen, Y. and Singpurwalla, N. D. (1995) A shot-noise model for software reliability. Proc. 50th Session of the Int. Statist. Inst. To appear.Google Scholar
Cox, D. R. and Isham, V. (1980) Point Processes. Chapman and Hall, London.Google Scholar
Crow, L. H. and Singpurwalla, N. D. (1984) An empirically developed Fourier series model for describing software failures. IEEE Trans. Reliability 33, 176–83.Google Scholar
Fakhre-Zakeri, I. and Slud, E. (1995) Mixture models for software reliability with imperfect debugging: identifiability of parameters. IEEE Trans. Reliability 44, 104–13.Google Scholar
Goel, A. L. and Okumoto, K. (1978) An analysis of recurrent software failures on a real-time control system. Proc. ACM Annual Tech-Conf., pp. 496–500.Google Scholar
Goel, A. L. and Okumoto, K. (1979) Time-dependent error detection rate model for software reliability and other performance measures. IEEE Trans. Reliability 28, 206–11.Google Scholar
Jelinski, Z. and Moranda, P. (1972) Software reliability research. In Statistical Computer Performance Evaluation. ed. Freiberger, W. Academic Press, New York. pp. 465–84.Google Scholar
Koch, G. and Sprey, P. J. C. (1983) A martingale approach to software reliability. IEEE Trans. Reliability 32, 342–2.Google Scholar
Littlewood, B. (1980) A Bayesian differential debugging model for software reliability. Proc. IEEE COMPSAC. Google Scholar
Littlewood, B. and Verall, J. L. (1973) A Bayesian reliability growth model for computer software. Appl. Statist. 22, 332–46.Google Scholar
Marshall, A. W. (1975) Some comments on the hazard gradients. Stoch. Proc. Appl. 3, 295–300.Google Scholar
Mazzuchi, T. A. and Soyer, R. (1988) A Bayes empirical-Bayes model for software reliability. IEEE Trans. Reliability 37, 248–54.CrossRefGoogle Scholar
Moranda, P. B. (1975) Prediction of software reliability and its applications. Proc. 1975 Annual Reliability and Maintainability Symp., pp. 327–32.Google Scholar
Musa, J. D. and Okumoto, K. (1984) A logarithmic Poisson execution time model for software reliability measurement. Proc. 7th Int. Conf. on Software Engineering, Orlando, pp. 230–37.Google Scholar
Van Pul, M. C. J. (1993) Statistical Analysis of Software Reliability Models. Rijksuniversiteit, Utrecht.Google Scholar
Sahinoglu, M. (1992) Compound-Poisson software reliability model. IEEE Trans. Software Eng. 18, 624–30.Google Scholar
Schick, G. J. and Wolverton, R. W. (1973) Assessment of software reliability. Proc. Operat. Res. Physica, Wien. pp. 395–422.Google Scholar
Schick, G. J. and Wolverton, R. W. (1978) An analysis of computing software reliability models. IEEE Trans. Software Eng. 4, 104–20.Google Scholar
Singpurwalla, N. D. (1995) The failure rate of software: does it exist? IEEE Trans. Reliability 44, 463–469.Google Scholar
Singpurwalla, N. D. and Soyer, R. (1985) Assessing (software) reliability growth using a random coefficient autoregressive process and its ramifications. IEEE Trans. Software Eng. 11, 1456–64.Google Scholar
Singpurwalla, N. D. and Soyer, R. (1992) Nonhomogeneous autoregressive processes for tracking (software) reliability growth, and their Bayesian analysis. J. R. Statist. Soc. B 54, 145–56.Google Scholar
Singpurwalla, N. D. and Wilson, S. P. (1994) Software reliability modeling. Int. Statist. Rev. 62, 289–317.CrossRefGoogle Scholar
Slud, E. (1995) Testing for imperfect debugging in software reliability. Technical Report MD-95-01-ES, TR95-01. University of Maryland.Google Scholar
Snyder, D. L. and Miller, M. I. (1991) Random Point Processes in Time and Space. Springer, New York.CrossRefGoogle Scholar
Xie, M. (1991) Software Reliability Modeling. World Scientific, Singapore.Google Scholar
Yamada, S. (1991) Software quality/reliability measurement and assessment: software reliability growth models and data analysis. J. Inf. Proc. 14, 254–66.Google Scholar