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Estimating software reliability using size-biased modelling.

Soumen Dey1, Ashis Kumar Chakraborty2

  • 1Norwegian University of Life Sciences, s, Norway.

Journal of Applied Statistics
|December 4, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel size-biased sampling approach to estimate software reliability and bug counts. The developed Bayesian model accurately predicts software defects and testing phases, enhancing software quality assurance.

Area of Science:

  • Software Engineering
  • Statistical Modeling
  • Reliability Engineering

Background:

  • Software testing is crucial for identifying bugs during development.
  • Estimating software reliability and total bug count remains a challenge.
  • Existing methods may not fully capture bug detection dynamics.

Purpose of the Study:

  • To propose a size-biased sampling framework for software reliability estimation.
  • To introduce the concept of 'eventual bug size' as a latent variable.
  • To develop and validate a Bayesian generalized linear mixed model (GLMM).

Main Methods:

  • Developed a Bayesian GLMM incorporating size-biased sampling.
  • The model treats bug detection probability as a function of eventual bug size.
Keywords:
Bayesian analysisSoftware reliabilitybug sizesize-biasedsoftware testingstopping phase

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  • Sensitivity analysis performed via simulation by varying inputs and detection probability.

    • Main Results:

    • The developed model accurately estimates key parameters for software reliability.
    • Simulation studies confirm the robustness of the parameter estimation.
    • The model was successfully applied to commercial and ISRO software testing datasets.

    Conclusions:

    • The size-biased sampling approach offers a unified framework for software reliability and bug estimation.
    • The Bayesian GLMM provides accurate predictions for software testing phases.
    • The hierarchical modeling approach has potential applications beyond software engineering, such as in hydrocarbon exploration.