Application of Neural Networks for Estimating Software Maintainability Using Object-Oriented Metrics

This paper presents the application of neural networks in software maintainability estimation using objectoriented metrics. Maintenance effort can be measured as the number of lines changed per class. In this paper, the number of lines changed per class (modification volume) is predicted using Ward neural network and General Regression neural network (GRNN). Object-oriented design metrics concerning with inheritance related measures, complexity measures, cohesion measures, coupling measures and size measures are applied in this study. Principal components, which are derived from these object-oriented metrics, are used as independent variables.

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