Prediction of Software Readiness Using Neural Network

Abstract

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.