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A New Reliability Model Based on Lindley Distribution with Application to Failure Data

Author

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  • Norah N. Al-Mutairi
  • Lutfiah I. Al-Turk
  • Sharifah A. Al-Rajhi

Abstract

Software reliability is an important feature that influences systems’ reliability. Software reliability models are a common tool to evaluate software reliability quantitatively. Various reliability models have been suggested based on the NHPP (nonhomogeneous Poisson process). In this article, a new NHPP model based on the Lindley distribution is proposed. The mathematical formulas for its measures of reliability are obtained and graphically illustrated. The proposed model’s parameters are estimated using both the NLSE (nonlinear least squares estimation) and the WNLSE (weighted nonlinear least squares estimation) methods. The model is then validated based on several different reliability datasets. The methods of estimation are evaluated and compared using three different criteria. The performance of the new model is also evaluated and compared, both objectively and subjectively, with three previously suggested models. The application results show that our new model demonstrates good performance in our selected failure data.

Suggested Citation

  • Norah N. Al-Mutairi & Lutfiah I. Al-Turk & Sharifah A. Al-Rajhi, 2020. "A New Reliability Model Based on Lindley Distribution with Application to Failure Data," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, November.
    • Handle: RePEc:hin:jnlmpe:4915812
    DOI: 10.1155/2020/4915812
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    Cited by:

    1. Qing Tian & Chih-Chiang Fang & Chun-Wu Yeh, 2022. "Software Release Assessment under Multiple Alternatives with Consideration of Debuggers’ Learning Rate and Imperfect Debugging Environment," Mathematics, MDPI, vol. 10(10), pages 1-24, May.
    2. Tabassum Naz Sindhu & Sadia Anwar & Marwa K. H. Hassan & Showkat Ahmad Lone & Tahani A. Abushal & Anum Shafiq, 2023. "An Analysis of the New Reliability Model Based on Bathtub-Shaped Failure Rate Distribution with Application to Failure Data," Mathematics, MDPI, vol. 11(4), pages 1-18, February.

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