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An efficient parameter estimation of software reliability growth models using gravitational search algorithm

Author

Listed:
  • Ankur Choudhary

    (Amity University Uttar Pradesh)

  • Anurag Singh Baghel

    (Gautam Buddha University)

  • Om Prakash Sangwan

    (Guru Jambheshwar University)

Abstract

This paper presents an effective parameter estimation approach for software reliability growth models using gravitational search algorithm. A software reliability growth model is imperfect, if model parameters are unknown and are not validated on real-time software datasets. There exist several efficient numerical estimation techniques for parameter estimation of software reliability growth models. But they are not panacea. Sample size, biasing and initialization etc. always remain a constraint for best parameter estimation. Results indicate that gravitational search algorithm based technique for parameter estimation overcomes these problems and does superior quality parameter estimation. In this paper, extensive experiments on nine real-time datasets were conducted and results were analyzed to compare the proposed approach. The analysis results point towards the superiority of proposed approach over existing numerical estimation, genetic algorithm and cuckoo search methods.

Suggested Citation

  • Ankur Choudhary & Anurag Singh Baghel & Om Prakash Sangwan, 2017. "An efficient parameter estimation of software reliability growth models using gravitational search algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 79-88, March.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-016-0541-0
    DOI: 10.1007/s13198-016-0541-0
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    References listed on IDEAS

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    1. P.K. Kapur & Hoang Pham & A. Gupta & P.C. Jha, 2011. "Software Reliability Assessment with OR Applications," Springer Series in Reliability Engineering, Springer, number 978-0-85729-204-9, February.
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    Cited by:

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    2. Joshua Steakelum & Jacob Aubertine & Kenan Chen & Vidhyashree Nagaraju & Lance Fiondella, 2022. "Multi-phase algorithm design for accurate and efficient model fitting," Annals of Operations Research, Springer, vol. 311(1), pages 357-379, April.

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