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A generic data-driven software reliability model with model mining technique

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  • Yang, Bo
  • Li, Xiang
  • Xie, Min
  • Tan, Feng

Abstract

Complex systems contain both hardware and software, and software reliability becomes more and more essential in system reliability context. In recent years, data-driven software reliability models (DDSRMs) with multiple-delayed-input single-output (MDISO) architecture have been proposed and studied. For these models, the software failure process is viewed as a time series and it is assumed that a software failure is strongly correlated with the most recent failures. In reality, this assumption may not be valid and hence the model performance would be affected. In this paper, we propose a generic DDSRM with MDISO architecture by relaxing this unrealistic assumption. The proposed model can cater for various failure correlations and existing DDSRMs are special cases of the proposed model. A hybrid genetic algorithm (GA)-based algorithm is developed which adopts the model mining technique to discover the correlation of failures and to obtain optimal model parameters. Numerical examples are presented and results reveal that the proposed model outperforms existing DDSRMs.

Suggested Citation

  • Yang, Bo & Li, Xiang & Xie, Min & Tan, Feng, 2010. "A generic data-driven software reliability model with model mining technique," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 671-678.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:6:p:671-678
    DOI: 10.1016/j.ress.2010.02.006
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    References listed on IDEAS

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    1. Hu, Q.P. & Xie, M. & Ng, S.H. & Levitin, G., 2007. "Robust recurrent neural network modeling for software fault detection and correction prediction," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 332-340.
    2. Ye, Zhisheng & Li, Zhizhong & Xie, Min, 2010. "Some improvements on adaptive genetic algorithms for reliability-related applications," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 120-126.
    3. Chiu, Kuei-Chen & Huang, Yeu-Shiang & Lee, Tzai-Zang, 2008. "A study of software reliability growth from the perspective of learning effects," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1410-1421.
    4. Chen, Kuan-Yu, 2007. "Forecasting systems reliability based on support vector regression with genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 423-432.
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    Cited by:

    1. 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.
    2. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
    3. Pievatolo, Antonio & Ruggeri, Fabrizio & Soyer, Refik, 2012. "A Bayesian hidden Markov model for imperfect debugging," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 11-21.
    4. Peng, R. & Li, Y.F. & Zhang, W.J. & Hu, Q.P., 2014. "Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 37-43.
    5. Utkin, Lev V. & Coolen, Frank P.A., 2018. "A robust weighted SVR-based software reliability growth model," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 93-101.
    6. Gaver, Donald P. & Jacobs, Patricia A., 2014. "Reliability growth by failure mode removal," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 27-32.

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