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A Framework For Discrete Software Reliability Modeling With Program Size And Its Applications

In: Recent Advances In Stochastic Operations Research

Author

Listed:
  • SHINI INOUE

    (Tottori University, 4-101 Minami, Koyama-cho, Tottori-shi, Tottori 680-8552, Japan)

  • SHIGERU YAMADA

    (Tottori University, 4-101 Minami, Koyama-cho, Tottori-shi, Tottori 680-8552, Japan)

Abstract

We discuss a generalization framework for discrete software reliability growth models (SRGMs) with the effect of program size on software reliability growth process. Our framework enables us to develop a plausible discrete software reliability growth model with program size by applying a suitable software failure-occurrence times distribution to our framework. In this chapter generalized software reliability assessment measures are also derived. After that, we discuss a parameter estimation method of our modeling framework discussed in this chapter. Additionally, we discuss optimal software release problems based on a SRGM developed by using our framework as one of the application issues. Finally, we depict numerical illustrations for the software reliability assessment measures and for derived optimal release policies by using actual fault count data.

Suggested Citation

  • Shini Inoue & Shigeru Yamada, 2007. "A Framework For Discrete Software Reliability Modeling With Program Size And Its Applications," World Scientific Book Chapters, in: Tadashi Dohi & Shunji Osaki & Katsushige Sawaki (ed.), Recent Advances In Stochastic Operations Research, chapter 5, pages 63-78, World Scientific Publishing Co. Pte. Ltd..
  • Handle: RePEc:wsi:wschap:9789812706683_0005
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    Cited by:

    1. Peng, R. & Li, Y.F. & Zhang, W.J. & Hu, Q.P., 2014. "Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 37-43.
    2. Subhashis Chatterjee & Ankur Shukla & Hoang Pham, 2019. "Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio, fault removal efficiency, and change point," Journal of Risk and Reliability, , vol. 233(2), pages 246-256, April.

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