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Non-parametric Estimation for NHPP Software Reliability Models

Author

Listed:
  • Zhiguo Wang
  • Jinde Wang
  • Xue Liang

Abstract

The non-homogeneous Poisson process (NHPP) model is a very important class of software reliability models and is widely used in software reliability engineering. NHPPs are characterized by their intensity functions. In the literature it is usually assumed that the functional forms of the intensity functions are known and only some parameters in intensity functions are unknown. The parametric statistical methods can then be applied to estimate or to test the unknown reliability models. However, in realistic situations it is often the case that the functional form of the failure intensity is not very well known or is completely unknown. In this case we have to use functional (non-parametric) estimation methods. The non-parametric techniques do not require any preliminary assumption on the software models and then can reduce the parameter modeling bias. The existing non-parametric methods in the statistical methods are usually not applicable to software reliability data. In this paper we construct some non-parametric methods to estimate the failure intensity function of the NHPP model, taking the particularities of the software failure data into consideration.

Suggested Citation

  • Zhiguo Wang & Jinde Wang & Xue Liang, 2007. "Non-parametric Estimation for NHPP Software Reliability Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 107-119.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:107-119
    DOI: 10.1080/02664760600994497
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    References listed on IDEAS

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    1. P. K. Kapur & H. Pham & A. Gupta & P. C. Jha, 2011. "Software Reliability Growth Models," Springer Series in Reliability Engineering, in: Software Reliability Assessment with OR Applications, chapter 0, pages 49-95, Springer.
    2. Pham, Hoang & Zhang, Xuemei, 2003. "NHPP software reliability and cost models with testing coverage," European Journal of Operational Research, Elsevier, vol. 145(2), pages 443-454, March.
    3. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
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