Reusability Prediction using Density Based Clustering

Software reuse is the process of implementing or updating software systems using existing software assets. Anything that is produced from a software development effort can potentially be reused. In this study, the performance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is evaluated for Reusability Prediction of Function based Software systems. Here, the metric based approach is used for prediction. Reusability value is expressed in the six linguistic values. Five Input metrics are used as Input and clusters are formed using DBSCAN, thereafter 10 fold cross validation performance of the system is recorded. The proposed technique is showing Accuracy value approximately equal to 86.238%, so it is satisfactory enough to use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique for the prediction of the function based reusable modules from the existing reservoir of software components. Keywords— DBSCAN, Reuse, Precision, Recall.