CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Enhanced Software Quality Metrics for Fault Prediction in Object Oriented Components using SVM Classifier

by C.neelamegam, M.punithvalli
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 22
Year of Publication: 2012
Authors: C.neelamegam, M.punithvalli
10.5120/7099-9834

C.neelamegam, M.punithvalli . Enhanced Software Quality Metrics for Fault Prediction in Object Oriented Components using SVM Classifier. International Journal of Computer Applications. 46, 22 ( May 2012), 30-36. DOI=10.5120/7099-9834

@article{ 10.5120/7099-9834,
author = { C.neelamegam, M.punithvalli },
title = { Enhanced Software Quality Metrics for Fault Prediction in Object Oriented Components using SVM Classifier },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 22 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number22/7099-9834/ },
doi = { 10.5120/7099-9834 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:20.654257+05:30
%A C.neelamegam
%A M.punithvalli
%T Enhanced Software Quality Metrics for Fault Prediction in Object Oriented Components using SVM Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 22
%P 30-36
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software quality metrics are defined as methods for quantitatively determining the extent to which an object oriented (OO) software process possess a certain quality attribute. Increase in software complexity and size is increasing the demand for new metrics to identify flaws in the design of OO system. This demand has necessitated this study to focus on adopting new metrics for measuring class complexities, for which established practices have yet to be developed. The proposed system works in two stages. The first stage presents new software metrics for measuring class complexity and the second stage analyzes the use of SVM classifier to predict faulty modules. Four new metrics, namely, Class Method Flow Complexity Measure, Friend Class Complexity Metric, Class Complexity from Inheritance and Class Complexity from Cohesion Measure, are proposed. These metrics, combined with 20 existing metrics, are used during prediction using SVM. The performance of prediction system is analyzed in terms of accuracy, precision, recall and F Measure. The experimental results showed positive improvement in the performance of prediction with the inclusion of the proposed metric and SVM classifier.

References
  1. Arockiam, L. and Aloysius, A. (2011) Attribute Weighted Class Complexity: A New Metric for Measuring Cognitive Complexity of OO Systems, World Academy of Science, Engineering and Technology, 58,Pp. 808-813.
  2. Bansiya, J. , Etzkorn, L. , Davis, C. and Li, W. (1999) A class cohesion metric for object-oriented designs, Journal of Object-Oriented Program, Vol. 11, No. 8, Pp. 47-52.
  3. Bellman, R. (1961) Adaptive Control Processes, Princeton University Press.
  4. Briand, L. C. , Devanbu, P. T. and Melo, W. L. (1997) An Investigation into Coupling Measures for C++, International Conference on Software Engineering, Pp. 412-421.
  5. Briand, L. C. , Morasca, S. and Basili, V. R. (1999) Defining and validating measures for object-based high-level design, IEEE Transactions on Software Engineering, Vol. 25, No. 5, Pp. 722-743.
  6. Catal, C and Diri, B. (2009) Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem, Information Science, Elsevier, Vol. 179, Pp. 1040-1058.
  7. Chowdhury, I. and Zulkernine, M. (2011) Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities, Journal of Systems Architecture, Elsevier, Vol. 57, Pp. 294-313.
  8. Counsell, S. and Newson, P. (2000) Use of Friends in C++ Software: An Empirical Investigation. Journal of Systems and Software, Vol. 53, No. 1, Pp. 15. 21.
  9. Counsell, S. , Newson, P. and Mendes, E. (2004) Design Level Hypothesis Testing Through Reverse Engineering of Object-Oriented Software, International Journal of Software Engineering, Vol. 14, No. 2, Pp. 207. 220.
  10. Counsell, S. , Swift, S. and Crampton, J. (2006) The interpretation and utility of three cohesion metrics for object-oriented design, ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 15, No. 2, Pp. 123-149.
  11. Dietterich, T. (1998) Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computation, Vol. 10, Pp. 1895–1924.
  12. English, M. , Buckley, J. , Cahill, T. and Lynch, K. (2005) An Empirical Study of the Use of Friends in C++ Software, International Workshop on Program Comprehension, Pp. 329. 332.
  13. Goh, T. H. and Wong, F. (1991) Semantic extraction using neural network modeling and sensitivity analysis, Proceedings of IEEE International Joint Conference on Neural Networks, Pp. 18–21.
  14. Henry, S. M. and Kafura, D. (1981) Software structure metrics based on information flow, IEEE Transactions on Sofware Engineering, Vol. SE-7, Pp. 510-518.
  15. Nadeau, C. and Bengio, Y. (2003) Inference for the generalization error, Machine Learning, Vol. 52, Pp. 239–281.
  16. Saltelli, A. , Chan, K. and Scott, E. M. (2000) Sensitivity Analysis, John Wiley & Sons.
  17. Wang. Y, (2002) On Cognitive Informatics, IEEE International Conference on Cognitive Informatics, Pp. 69-74.
  18. Weyuker. E. J. (1988) Evaluating Software Complexity Measures, IEEE Transactions on Software Engineering, Vol. 14, No. 9, Pp. 1357-1365.
Index Terms

Computer Science
Information Sciences

Keywords

Class Complexity Object Oriented Quality Metrics Software Fault Prediction Support Vector Machine.

Powered by PhDFocusTM