Application of neural networks for software quality prediction using object-oriented metrics

This paper presents the application of neural networks in software quality estimation using object-oriented metrics. In this paper, two kinds of investigation are performed. The first oil predicting the number of defects in a class and the second on predicting the number of lines changed per class. Two neural network models are used, they are Ward neural network and General Regression neural network (GRNN). Object-oriented design metrics concerning inheritance related measures, complexity measures, cohesion measures. coupling measures and memory allocation measures are used Lis the independent variables. GRNN network model is found to predict more accurately than Ward network model. (C) 2004 Elsevier Inc. All rights reserved.