Software Quality Prediction Method with Hybrid Applying Principal Components Analysis and Wavelet Neural Network and Genetic Algorithm

The principal components analysis method is used to select the training and testing data from software metrics to eliminate the multicollinearitiy in the original data and reduce the dimensions of the data, the genetic algorithm is applied to optimize the parameters of the constructed wavelet neural network, and then a novel software quality prediction model is proposed and its performance is evaluated. The experimental results with six error parameters show that the presented model combining principal components analysis with wavelet neural network and genetic algorithm can obtain better prediction accuracy than the models using the neural network of back propagation and generalized regression neural network.

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