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In the past 35 years more than 100 software reliability models are proposed. Most of them are parametric models. In this paper we present a comparative study of different non-parametric models based on the neural networks and regression model learned by the real coded genetic algorithm to predict the cumulative failure in the software. Experimental results show that the training of different models by our real coded genetic algorithm have a good predictive capability across different projects.