Prediction of software reliability: a comparison between regression and neural network non-parametric models

In this paper, neural networks have been proposed as an alternative technique to build software reliability growth models. A feedforward neural network was used to predict the number of faults initially resident in a program at the beginning of a test/debug process. To evaluate the predictive capability of the developed model, data sets from various projects were used. A comparison between regression parametric models and neural network models is provided.