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Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1365-1383页

作者： Pratik Roy Ghanshaym Singha Mahapatra kashi nath dey Department of Computer Science and Engineering University of CalcuttaKolkata-700106India Department of Mathematics National Institute of TechnologyPuducherryKaraikal-609609India

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.

关键词： Artificial neural network(ANN) fuzzy particle swarm optimization(PSO) reliability prediction software reliability

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