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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2019  >  6(6): 1365-1383
Pratik Roy, Ghanshaym Singha Mahapatra and Kashi Nath Dey, "Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1365-1383, Nov. 2019. doi: 10.1109/JAS.2019.1911753
Citation: Pratik Roy, Ghanshaym Singha Mahapatra and Kashi Nath Dey, "Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1365-1383, Nov. 2019. doi: 10.1109/JAS.2019.1911753
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Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

doi: 10.1109/JAS.2019.1911753
Funds:  This work was supported by the Council of Scientific and Industrial Research of India (09/028(0947)/2015-EMR-I)
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    Abstract
  • This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.

     

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    • References(42)
  • [1]
    M. R. Lyu, Handbook of Software Reliability Engineering, New York, McGraw-Hill, 1996.
    [2]
    M. Jain and R. Gupta, " Optimal release policy of module-based software,” Quality Technology and Quantitative Management, vol. 8, no. 2, pp. 147–165, 2011. doi: 10.1080/16843703.2011.11673253
    [3]
    A. L. Goel and K. Okumoto, " Time-dependent error-detection rate model for software reliability and other performance measures,” IEEE Trans. Reliability, vol. 28, no. 3, pp. 206–211, 1979.
    [4]
    M. Xie, " Software Reliability Modeling,” World Scientific,Singapore, 1991.
    [5]
    H. Pham, System Software Reliability, New Jersey, Springer, 2006.
    [6]
    S. Yamada and S. Osaki, " Software reliability growth modeling: models and applications,” IEEE Trans. Software Engineering, vol. 11, pp. 1431–1437, 1985.
    [7]
    J. Yang, Y. Liu, M. Xie, and M. Zhao, " Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes,” J. Systems and Software, vol. 115, pp. 102–110, 2016. doi: 10.1016/j.jss.2016.01.025
    [8]
    M. Ohba and X.M. Chou, Does imperfect debugging effect software reliability growth, in Proc. 11th Int. Conf. on Software Engineering, 1989, pp. 237–244.
    [9]
    T. Lynch, H. Pham, and W. Kuo, " Modeling software-reliability with multiple failure-types and imperfect debugging, ” in Proc. Annual Reliability and Maintainability Symposium, 1994, pp. 235–240.
    [10]
    Q. Li and H. Pham, " NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage,” Applied Mathematical Modelling, vol. 51, pp. 68–85, 2017. doi: 10.1016/j.apm.2017.06.034
    [11]
    B. Pachauri, A. Kumar, and J. Dhar, " Modeling optimal release policy under fuzzy paradigm in imperfect debugging environment,” Information and Software Technology, vol. 55, no. 11, pp. 1974–1980, 2013. doi: 10.1016/j.infsof.2013.06.001
    [12]
    S. M. Li, Q. Yin, P. Guo, and M. R. Lyu, " A hierarchical mixture model for software reliability prediction,” Applied Mathematics and Computation, vol. 185, pp. 1120–1130, 2007. doi: 10.1016/j.amc.2006.07.028
    [13]
    Y. S. Su and C. Y. Huang, " Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models,” J. Systems and Software, vol. 80, pp. 606–615, 2007. doi: 10.1016/j.jss.2006.06.017
    [14]
    J. Zheng, " Predicting software reliability with neural network ensembles,” Expert Systems with Applications, vol. 36, pp. 2116–2122, 2009. doi: 10.1016/j.eswa.2007.12.029
    [15]
    P. K. Kapur, S. K. Khatri, and M. Basirzadeh, " Software reliability assessment using artificial neural network based flexible model incorporating faults of different complexity, International Journal of Reliability,” Quality and Safety Engineering, vol. 15, no. 2, pp. 113–127, 2008. doi: 10.1142/S0218539308002976
    [16]
    C. J. Hsu and C. Y. Huang, " Optimal weighted combinational models for software reliability estimation and analysis,” IEEE Trans. Reliability, vol. 63, no. 3, pp. 731–749, 2014. doi: 10.1109/TR.2014.2315966
    [17]
    J. Kennedy and R. C. Eberhart, " Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks, 1995, pp. 1942–1948.
    [18]
    K. E. Parsopoulos and M. N. Vrahatis, " Particle swarm optimization and intelligence: advances and applications,” Information Science Reference, 2010.
    [19]
    M. S. Arumugam and M. V. C. Ra, " On the improved performances of the particle swarm optimization algorithms with adaptive parameters, crossover operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems,” Applied Soft Computing, vol. 8, no. 1, pp. 324–336, 2008. doi: 10.1016/j.asoc.2007.01.010
    [20]
    M. Nasir, S. Das, D. Maity, S. Sengupta, U. Halder, and P. N. Suganthan, " A dynamic neighborhood learning based particle swarm optimizer for global numerical optimizatio,” Information Sciences, vol. 209, pp. 16–36, 2012. doi: 10.1016/j.ins.2012.04.028
    [21]
    H. Wang, H. Sun, C. Li, S. Rahnamayan, and J. S. Pan, " Diversity enhanced particle swarm optimization with neighborhood search,” Information Sciences, vol. 223, pp. 119–135, 2013. doi: 10.1016/j.ins.2012.10.012
    [22]
    G. Xu, " An adaptive parameter tuning of particle swarm optimization algorithm,” Applied Mathematics and Computation, vol. 219, pp. 4560–4569, 2013. doi: 10.1016/j.amc.2012.10.067
    [23]
    T. Niknam, " A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem,” Applied Energy, vol. 87, pp. 327–339, 2010. doi: 10.1016/j.apenergy.2009.05.016
    [24]
    Y. T. Juang, S. L. Tung, and H. C. Chiu, " Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,” Information Sciences, vol. 181, pp. 4539–4549, 2011. doi: 10.1016/j.ins.2010.11.025
    [25]
    E. Naderi, H. Narimani, M. Fathi, and M. R. Narimani, " A novel fuzzy adaptive configuration of particle swarm optimization to solve largescale optimal reactive power dispatch,” Applied Soft Computing, vol. 53, pp. 441–456, 2017. doi: 10.1016/j.asoc.2017.01.012
    [26]
    J. R. Zhang, J. Zhang, T. M. Lok, and M. R. Lyu, " A hybrid particle swarm optimization-backpropagation algorithm for feedforward neural network training,” Applied Mathematics and Computation, vol. 185, no. 2, pp. 1026–1037, 2007. doi: 10.1016/j.amc.2006.07.025
    [27]
    S. Haykin, Neural Networks and Learning Machines, New York, Prentice Hall, 2012.
    [28]
    N. Karunanithi, D. Whitley, and Y. K. Malaiya, " Prediction of software reliability using connectionist models,” IEEE Trans. Software Engineering, vol. 18, pp. 563–574, 1992. doi: 10.1109/32.148475
    [29]
    K. Y. Cai, L. Cai, W. D. Wang, Z. Y. Yu, and D. Zhang, " On the neural network approach in software reliability modeling,” J. Systems and Software, vol. 58, pp. 47–62, 2001. doi: 10.1016/S0164-1212(01)00027-9
    [30]
    S. L. Ho, M. Xie, and T. N. Goh, " A study of the connectionist models for software reliability prediction,” Computers and Mathematics with Applications, vol. 46, pp. 1037–1045, 2003. doi: 10.1016/S0898-1221(03)90117-9
    [31]
    L. Tian and A. Noore, " On-line prediction of software reliability using an evolutionary connectionist model,” J. Systems and Software, vol. 77, pp. 173–180, 2005. doi: 10.1016/j.jss.2004.08.023
    [32]
    Q. P. Hu, M. Xie, S. H. Ng, and G. Levitin, " Robust recurrent neural network modeling for software fault detection and correction prediction,” Reliability Engineering and System Safety, vol. 92, pp. 332–340, 2007. doi: 10.1016/j.ress.2006.04.007
    [33]
    N. R. Kiran and V. Ravi, " Software reliability prediction by soft computing techniques,” J. Systems and Software, vol. 81, no. 4, pp. 576–583, 2008. doi: 10.1016/j.jss.2007.05.005
    [34]
    P. K. Kapur, V. S. S. Yadavalli, S. K. Khatri, and M. Basirzadeh, " Enhancing software reliability of a complex software system architecture using artificial neural-networks ensemble,” Int. J. Reliability Quality and Safety Engineering, vol. 18, no. 3, pp. 271–284, 2011. doi: 10.1142/S0218539311004135
    [35]
    R. Mohanty, V. Ravi, and M. R. Patra, " Hybrid intelligent systems for predicting software reliability,” Applied Soft Computing, vol. 13, no. 1, pp. 189–200, 2013. doi: 10.1016/j.asoc.2012.08.015
    [36]
    O. F. Arar and K. Ayan, " Software defect prediction using cost-sensitive neural network,” Applied Soft Computing, vol. 33, pp. 263–277, 2015. doi: 10.1016/j.asoc.2015.04.045
    [37]
    P. Roy, G. S. Mahapatra, and K. N. Dey, " An efficient particle swarm optimization-based neural network approach for software reliability assessment,” Int. J. Reliability,Quality and Safety Engineering, vol. 24, no. 4, pp. 1–24, 2017.
    [38]
    Y. Shi and R. C. Eberhart, " A modified particle swarm optimizer,” in Proc. IEEE World Congr. on Computational Intelligence, 1998, pp. 69–73.
    [39]
    Y. Marinakis, A. Migdalas, and A. Sifaleras, " A hybrid particle swarm optimization – variable neighborhood search algorithm for constrained shortest path problems,” European J. Operational Research, vol. 261, pp. 819–834, 2017. doi: 10.1016/j.ejor.2017.03.031
    [40]
    C. Jin and S. W. Jin, " Parameter optimization of software reliability growth model with S-shaped testing-effort function using improved swarm intelligent optimization,” Applied Soft Computing, vol. 40, pp. 283–291, 2016. doi: 10.1016/j.asoc.2015.11.041
    [41]
    P. N. Misra, " Softawre reliability analysis,” IBM Systems J., vol. 22, pp. 262–270, 1983. doi: 10.1147/sj.223.0262
    [42]
    J. D. Musa, A. Iannino, and K. Okumoto, Software Reliability Measurement, Prediction and Application, New York, McGraw-Hill, 1987.
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    Highlights

    • ANN based software reliability model trained by novel PSO algorithm is proposed.
    • Model developed considering fault generation during testing with fault complexity.
    • Neighborhood based fuzzy PSO algorithm is proposed for competent learning of ANN.
    • Fitting and prediction performances are compared with existing models.
    • Proposed model has better fitting and predictive ability than other models.
    • Faster release of software is achievable by using proposed model.

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