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Software reliability prediction by recurrent artificial chemical link network

Author

Listed:
  • Ajit Kumar Behera

    (Utakal University)

  • Mrutyunjaya Panda

    (Utakal University)

  • Satchidananda Dehuri

    (Fakir Mohan University)

Abstract

Software reliability prediction is the foremost challenge in software quality assurance. Several models have been developed that effectively assess software reliability, but no single model produces accurate prediction results in all situations. This paper proposes a recurrent chemical functional link artificial neural network model to predict the software reliability, where the parameters of the model are estimated by chemical reaction optimization. The proposed model is inheriting the best attributes of functional link artificial neural networks and recurrent neural networks which dynamically modeling a nonlinear system for software reliability prediction. The proposed model is analyzed using ten real-world software failure data. A time-series approach with logarithmic scaling has been adopted for the proper distribution of input data. Statistical analysis reveals that the proposed model exhibits superior performance.

Suggested Citation

  • Ajit Kumar Behera & Mrutyunjaya Panda & Satchidananda Dehuri, 2021. "Software reliability prediction by recurrent artificial chemical link network," 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. 12(6), pages 1308-1321, December.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:6:d:10.1007_s13198-021-01276-8
    DOI: 10.1007/s13198-021-01276-8
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    References listed on IDEAS

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    1. Littlewood, Bev & Salako, Kizito & Strigini, Lorenzo & Zhao, Xingyu, 2020. "On reliability assessment when a software-based system is replaced by a thought-to-be-better one," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    2. Arunima Jaiswal & Ruchika Malhotra, 2018. "Software reliability prediction using machine learning techniques," 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. 9(1), pages 230-244, February.
    3. B. Tirimula Rao & Satchidananda Dehuri & Rajib Mall, 2012. "Functional Link Artificial Neural Networks for Software Cost Estimation," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 3(2), pages 62-82, April.
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