Statistical Science

On Software and System Reliability Growth and Testing

Frank P. A. Coolen

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Abstract

Singpurwalla presents an insightful proposal on foundations of reliability [Statist. Sci. 31 (2016) 521–540], suggesting to consider reliability not as a probability but as a propensity, in particular as the unobservable parameter in De Finetti’s famous representation theorem. One specific issue considered is reliability growth, with example scenario the performance of software as it evolves over time. We briefly discuss some related aspects, mainly based on applied research on statistical methods to support software testing and insights from our research on system reliability.

Article information

Source
Statist. Sci., Volume 31, Number 4 (2016), 541-544.

Dates
First available in Project Euclid: 19 January 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1484816581

Digital Object Identifier
doi:10.1214/16-STS583

Mathematical Reviews number (MathSciNet)
MR3598734

Zentralblatt MATH identifier
06946246

Keywords
Reliability growth software testing system reliability

Citation

Coolen, Frank P. A. On Software and System Reliability Growth and Testing. Statist. Sci. 31 (2016), no. 4, 541--544. doi:10.1214/16-STS583. https://projecteuclid.org/euclid.ss/1484816581


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References

  • [1] Coolen, F. P. A. (2012). On some statistical aspects of software testing and reliability. In Complex Systems and Dependability (W. Zamojski, J. Mazurkiewicz, J. Sugier, T. Walkowiak and J. Kacprzyk, eds.) 103–113. Springer, Berlin.
  • [2] Coolen, F. P. A. and Coolen-Maturi, T. (2012). On generalizing the signature to systems with multiple types of components. In Complex Systems and Dependability (W. Zamojski, J. Mazurkiewicz, J. Sugier, T. Walkowiak and J. Kacprzyk, eds.) 115–130. Springer, Berlin.
  • [3] Coolen, F. P. A. and Coolen-Maturi, T. (2016). The structure function for system reliability as predictive (imprecise) probability. Reliab. Eng. Syst. Saf. 154 180–187.
  • [4] Coolen, F. P. A., Goldstein, M. and Munro, M. (2001). Generalized partition testing via Bayes linear methods. Inf. Softw. Technol. 43 783–793.
  • [5] Coolen, F. P. A., Goldstein, M. and Wooff, D. A. (2007). Using Bayesian statistics to support testing of software systems. J. Risk Reliab. 221 85–93.
  • [6] Coolen-Maturi, T. and Coolen, F. P. A. (2011). Unobserved, re-defined, unknown or removed failure modes in competing risks. J. Risk Reliab. 225 461–474.
  • [7] Samaniego, F. J. (2007). System Signatures and Their Applications in Engineering Reliability. International Series in Operations Research & Management Science 110. Springer, New York.
  • [8] Singpurwalla, N. D. (2016). Filtering and tracking survival propensity (reconsidering the foundations of reliability). Statist. Sci. 31 521–540.
  • [9] Wooff, D. A., Goldstein, M. and Coolen, F. P. A. (2002). Bayesian graphical models for software testing. IEEE Trans. Softw. Eng. 28 510–525.

See also

  • Main article: Filtering and Tracking Survival Propensity (Reconsidering the Foundations of Reliability).