A Neuro-Fuzzy Based Approach for the Prediction of Quality of Reusable Software Components

The requirement to improve software productivity has promoted the research on software metric technology. There are metrics for identifying the quality of reusable components. These metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the component and hence improve the productivity due to probabilistic increase in the reuse level. A suit of metrics can be used to obtain the reusability in the modules. And the reusability can be obtained with the help of Neuro-fuzzy based approach where neural network can learn new relationships with new input data, can be used to refine fuzzy rules to create fuzzy adaptive system. An algorithm has been proposed in which the inputs can be given to Neuro-fuzzy system in form of Cyclometric Complexity, Volume, Regularity, Reuse-Frequency & Coupling, and output call be obtained in terms of reusability.