Improving neural network predictions of software quality using principal components analysis

The application of statistical modeling techniques has been an intensely pursued area of research in the field of software engineering. The goal has been to model software quality and use that information to better understand the software development process. Neural network modeling methods have been applied to this field. The results reported indicate that neural network models have better predictive quality than some statistical models when predicting reliability and the number of faults. In this paper, we will explore the application of principal components analysis to neural network modeling as a way of improving the predictive quality of neural network quality models. We trained two neural nets with data collected from a large commercial software system, one with raw data, and one with principal components. Then, we compare the predictive quality of the two competing neural net models