Prediction of Software Readiness Using Neural Network
Jon T.S. Quah, Mie Mie, Thet Thwin · 2002
In this paper, we explore the behaviour of neural network in predicting software readiness. Our neural network model aims to predict the number of faults (including object- oriented faults) of a software under development. We use Ward neural network that is a backpropagation network with different activation functions. Different activation functions are applied to hidden layer slabs to detect different features in a pattern processed through a network. In our experiments, hyperbolic tangent, Gaussian, Gaussian-complement and linear functions are used as activation functions to improve prediction. This paper also compares the prediction results from multiple regression model and neural network model. Object-oriented design metrics are used as the independent variables in our study. Our study is conducted on three industrial real-time systems that contain a number of natural faults that has been reported over a period of three years.