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This paper proposes a discriminant analysis method that uses a neural network model to predict the fault-prone modules. Since the relation between fault-proneness and predictor variables are complicated in real software, conventional linear discriminant model is not suitable for prediction model. Therefore, this thesis uses a layered neural network to represent nonlinear relation between fault-proneness and predictor variables.
To evaluate the method, I have measured many kinds of metrics from large-scale software that have been maintained more than 20 years, and also measured the number of faults found after the release. Result of the evaluation showed that prediction accuracy of my model is better than that of conventional linear model.