doi: 10.7763/IJET.2011.V3.267
Quality Prediction of Object Oriented Software Using Density Based Clustering Approach
Supreet Kaur
,
- Dept.), DAV Institute of Engineering & Technology, Jalandhar, India
Abstract
A software 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.
Keywords
- DBSCAN
- Software Quality
- Fault Proneness
- NASA fault dataset
How to Cite
Supreet Kaur and Dinesh Kumar, "Quality Prediction of Object Oriented Software Using Density Based Clustering Approach," International Journal of Engineering and Technology, vol. 3, no. 4, pp. 440-445, 2011. https://doi.org/ 10.7763/IJET.2011.V3.267
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).