Software reliability prediction model based on ICA algorithm and MLP neural network

To achieve the high performance system without any failure, we should provide the high reliability level of software. Soft computing models for software reliability prediction suffer from low accuracy during predicting the number of faults. Moreover, the models have some problems like no solid mathematical foundation for analysis, being trapped in local minima, and convergence problem. This paper introduces Imperialist Competitive Algorithm (ICA) to overcome the weaknesses of previous models and improve the efficiency of training process of Multi-Layer Perceptron (MLP) neural network. Therefore, the network can predict the number of faults precisely. The results show that the proposed predicting model is more efficient than the existing techniques in prediction performance.