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

The paper presents the application of neural networks in software quality estimation using object-oriented metrics. Quality estimation includes estimating reliability as well as maintainability of software. Reliability is typically measured as the number of defects. Maintenance effort can be measured as the number of lines changed per class. In this paper, two kinds of investigation are performed: predicting the number of defects in a class; and predicting the number of lines change 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 as the independent variables. GRNN network model is found to predict more accurately than Ward network model.

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