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PERMMA: Enhancing parameter estimation of software reliability growth models: A comparative analysis of metaheuristic optimization algorithms

Author

Listed:
  • Vishal Pradhan
  • Arijit Patra
  • Ankush Jain
  • Garima Jain
  • Ajay Kumar
  • Joydip Dhar
  • Anjan Bandyopadhyay
  • Saurav Mallik
  • Naim Ahmad
  • Ahmed Said Badawy

Abstract

Software reliability growth models (SRGMs) are universally admitted and employed for reliability assessment. The process of software reliability analysis is separated into two components. The first component is model construction, and the second is parameter estimation. This study concentrates on the second segment parameter estimation. The past few decades of literature observance say that the parameter estimation was typically done by either maximum likelihood estimation (MLE) or least squares estimation (LSE). Increasing attention has been noted in stochastic optimization methods in the previous couple of decades. There are various limitations in the traditional optimization criteria; to overcome these obstacles metaheuristic optimization algorithms are used. Therefore, it requires a method of search space and local optima avoidance. To analyze the applicability of various developed meta-heuristic algorithms in SRGMs parameter estimation. The proposed approach compares the meta-heuristic methods for parameter estimation by various criteria. For parameter estimation, this study uses four meta-heuristics algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA). Four popular SRGMs did the comparative analysis of the parameter estimation power of these four algorithms on three actual-failure datasets. The estimated value of parameters through meta-heuristic algorithms are approximately near the LSE method values. The results show that RGA and GWO are better on a variety of real-world failure data, and they have excellent parameter estimation potential. Based on the convergence and R2 distribution criteria, this study suggests that RGA and GWO are more appropriate for the parameter estimation of SRGMs. RGA could locate the optimal solution more correctly and faster than GWO and other optimization techniques.

Suggested Citation

  • Vishal Pradhan & Arijit Patra & Ankush Jain & Garima Jain & Ajay Kumar & Joydip Dhar & Anjan Bandyopadhyay & Saurav Mallik & Naim Ahmad & Ahmed Said Badawy, 2024. "PERMMA: Enhancing parameter estimation of software reliability growth models: A comparative analysis of metaheuristic optimization algorithms," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-26, September.
    • Handle: RePEc:plo:pone00:0304055
    DOI: 10.1371/journal.pone.0304055
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    References listed on IDEAS

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