Quality Prediction of Object Oriented Software Using Density Based Clustering Approach

Abstract

A softw are fault prediction is a proven technique in achieving high software reliability. Prediction of fault-prone modules provides one way to support software quality engineering through improved scheduling and project control. Quality of software is increasingly important and testing related issues are becoming crucial for software. This necessitates the need to develop a real-time assessment technique that classifies these dynamically generated systems as being faulty/fault-free. A variety of software fault predictions techniques have been proposed, but none has proven to be consistently accurate. These techniques include statistical method, machine learning methods, parametric models and mixed algorithms. Therefore, there is a need to find the best techniques for Quality prediction of the software systems by finding the fault proneness. In this study, the performance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is evaluated for Java based Object Oriented Software system from NASA Metrics Data Program (MDP) data repository on the basis of fault proneness of the classes.