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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Analysis of incorporating modified Weibull model fault detection rate function into software reliability modeling.

Tabassum Naz Sindhu1, Anum Shafiq2,3, Zakia Hammouch4,5,6

  • 1Department of Statistics, Quaid-i-Azam University, Islamabad, 44000, Pakistan.

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|July 29, 2024
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Summary

This study introduces a new software reliability model to address varying field environments. The model enhances accuracy in predicting software failures, outperforming existing methods.

Keywords:
Maximum likelihood estimationMean value functionNHPPReliability efficiencySensitivity analysis

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Area of Science:

  • Computer Science
  • Software Engineering
  • Reliability Engineering

Background:

  • Software systems face reliability challenges when deployed in diverse field environments beyond controlled testing conditions.
  • Environmental variability and internal code defects complicate efforts to accurately assess and improve software reliability.
  • Existing models often struggle to account for the uncertainty introduced by different operating locations.

Purpose of the Study:

  • To propose a novel software reliability model that explicitly incorporates the uncertainty of operating environments.
  • To provide a closed-form solution for the mean value function of the proposed model.
  • To evaluate the model's performance against established methods using real-world software failure data.

Main Methods:

  • Development of a new software reliability model designed to handle environmental uncertainties.
  • Derivation of the explicit closed-form mean value function for the proposed model.
  • Comparative analysis against the nonhomogeneous Poisson process (NHPP) model based on the Weibull distribution using four failure data sets.

Main Results:

  • The proposed model demonstrated superior goodness of fit compared to the NHPP Weibull model across different estimation techniques.
  • The model showed high accuracy and provided a more detailed evaluation of software reliability.
  • Sensitivity analysis confirmed that the model's parameters significantly influence the mean value function.

Conclusions:

  • The new software reliability model effectively addresses environmental uncertainties, offering improved accuracy.
  • The model's versatility and superior performance make it a valuable tool for software reliability assessment.
  • Further research can explore the impact of specific environmental factors on model parameters.