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Software Reliability Model Related to Total Number of Faults Under Imperfect Debugging

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Advances in Intelligent Automation and Soft Computing (IASC 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 80))

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Abstract

In the current research on software reliability models, the total number of faults and its relevance to imperfect debugging are not sufficiently emphasized or considered. To solve this problem, we firstly classify and analyze the total number of faults in the current reliability models, and then establish a generalized imperfect debugging framework model that considers fault detection and repair. On this basis, we further consider the phenomenon of introducing new faults in debugging and establish concrete imperfect debugging models from the perspective of incremental changes in the total number of faults. By using 12 publicly published failure data sets (FDS) widely used to evaluate the reliability models, we demonstrate the overall performance of the proposed models. The results show that the proposed models have an excellent account in fitting and prediction, which proves their effectiveness and flexibility.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology At Weihai, Weihai, 264209, China

    Ce Zhang, Yufei Yuan, Wenqian Jiang, Zhichao Sun, Miaomiao Fan, Wenyu Li, Yafei Wen, Wen Song & Kaiwei Liu

  2. School of Computer and Control Engineering, Yantai University, Yantai, 264005, China

    Ye Ding

Authors
  1. Ce Zhang
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  2. Yufei Yuan
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  3. Wenqian Jiang
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  4. Zhichao Sun
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  5. Ye Ding
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  6. Miaomiao Fan
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  7. Wenyu Li
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  8. Yafei Wen
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  9. Wen Song
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  10. Kaiwei Liu
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Editors and Affiliations

  1. Electronics and Computer Engineering Technology, Indiana State University, Terre Haute, IN, USA

    **aolong Li

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Zhang, C. et al. (2022). Software Reliability Model Related to Total Number of Faults Under Imperfect Debugging. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_7

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