The scaling problem in neural networks for software reliability prediction

Recently, neural networks have been applied for software reliability growth prediction. Although the predictive capability of the neural network models are better than some of the well known analytic models, the scaling problem has not been completely addressed yet. With the present neural network models, it is necessary to scale the cumulative faults over a 0.0 to 1.0 range, so the user has to estimate in advance a maximum value for the total number of faults to be detected at the end of the test phase. In practice, such an estimate may not be accurate. Use of an inaccurate value for scaling the cumulative faults can severely affect the predictive capability of neural network models. This paper presents a solution to the scaling problem which uses a clipped linear unit in the output layer. With a clipped linear output unit, the neural networks can predict positive values in any unbounded range. The authors demonstrate the applicability of the proposed network structure with three data sets and compare its predictive accuracy with that of earlier models. Expressions for the failure rate process represented by the models of the proposed network structure are also derived