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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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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

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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  • 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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    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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