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Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio, fault removal efficiency, and change point

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  • Subhashis Chatterjee
  • Ankur Shukla
  • Hoang Pham

Abstract

Software reliability growth models have been proposed to assess and predict the reliability growth of software, remaining number of faults, and failure rate. In previous studies, software faults have been mainly categorized into two categories based on its severity in removal process: simple faults and hard faults. In reality, fault detectability is one of the crucial factors which can influence the reliability growth of software. The detectability of a software fault depends on how frequently the instructions containing faults are executed. However, fault removability of a software fault depends on fault removal efficiency of debugging team. The main motive of this article is to incorporate the fault detectability in software reliability assessment. Fault exposure ratio is an essential factor for software reliability modeling that controls the per-fault hazard rate. It is strongly dependent on fault detectability. In this article, the effect of fault detectability, fault removability, fault exposure ratio, and fault removal efficiency has been considered simultaneously in software reliability growth modeling. Moreover, a logistic fault exposure ratio has been introduced. The effect of change point is incorporated in the proposed software reliability growth model. Two illustrative examples with software testing data have been presented.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:2:p:246-256
    DOI: 10.1177/1748006X18772930
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    References listed on IDEAS

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    1. 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..
    2. Subhashis Chatterjee & Ankur Shukla, 2017. "An Ideal Software Release Policy for an Improved Software Reliability Growth Model Incorporating Imperfect Debugging with Fault Removal Efficiency and Change Point," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(03), pages 1-21, June.
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    7. Subhashis Chatterjee & Shobhit Nigam & Jeetendra Bahadur Singh & Lakshmi Narayan Upadhyaya, 2012. "Effect of change point and imperfect debugging in software reliability and its optimal release policy," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 18(5), pages 539-551, March.
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    Cited by:

    1. Da Hye Lee & In Hong Chang & Hoang Pham, 2020. "Software Reliability Model with Dependent Failures and SPRT," Mathematics, MDPI, vol. 8(8), pages 1-14, August.
    2. Vishal Pradhan & Ajay Kumar & Joydip Dhar, 2022. "Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time," Journal of Risk and Reliability, , vol. 236(1), pages 18-36, February.
    3. Umashankar Samal & Ajay Kumar, 2024. "A software reliability model incorporating fault removal efficiency and it’s release policy," Computational Statistics, Springer, vol. 39(6), pages 3137-3155, September.

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