The Detection of Fault-Prone Program Using a Neural Network

This paper proposes a discriminant analysis method that uses a neural network model to predict the fault-prone program modules that will cause failure after the release. In our method, neural networks of a layered type are used to represent nonlinear relation among predictor variables and objective variables. Since the relation among predictor variables and objective variables is complicated in real software, linear representation used in conventional discriminant analysis is not suitable for the prediction model. To evaluate the method, we have measured 20 metrics, as predictor variables, from a large scale software that have been maintained more than 20 years, and also measured the number of faults found after the release as objective variables. Result of the evaluation showed that prediction accuracy of our model is better than that of conventional linear model.