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Study on Software Process Metrics using Data Mining Tool -A Rough Set Theory Approach

by V. Jeyabalaraja, T. Edwin Prabakaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 18
Year of Publication: 2012
Authors: V. Jeyabalaraja, T. Edwin Prabakaran
10.5120/7285-0345

V. Jeyabalaraja, T. Edwin Prabakaran . Study on Software Process Metrics using Data Mining Tool -A Rough Set Theory Approach. International Journal of Computer Applications. 47, 18 ( June 2012), 1-5. DOI=10.5120/7285-0345

@article{ 10.5120/7285-0345,
author = { V. Jeyabalaraja, T. Edwin Prabakaran },
title = { Study on Software Process Metrics using Data Mining Tool -A Rough Set Theory Approach },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 18 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number18/7285-0345/ },
doi = { 10.5120/7285-0345 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:09.711236+05:30
%A V. Jeyabalaraja
%A T. Edwin Prabakaran
%T Study on Software Process Metrics using Data Mining Tool -A Rough Set Theory Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 18
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software industries are optimizing their resources to obtain the best quality software from minimum cost through identifying the potential resource for the assignments. The process matrices' are supported to increase the efficiency of the project in the development scenario. The process metrics functionalities are determined as per the development of code in terms of line of code or the size of the code which is developed by the developers without the error or the minimal development error. This paper aimed to identify the competence of the developers in their selected skill set relevant to the assigned tasks. It provides the developers those who are worked in the minimum skill set components and identified as a weak set of employees through equivalence algorithm of the rough set theory. The functional attributes are observed and analyzed to find out the maximal error process which leads to the identification employees set those are made more modification with low level expert knowledge in the working area or project. The observation, analysis and the experimental procedures using Quick reduct are presented in this paper.

References
  1. A. E. Hassanien, Z. Suraj, D. Slezak, and P. Lingras, Rough Computing: Theories, Technologies, and Applications, New York: Information Science Reference, 2008.
  2. A. Skowron, Z. Pawlak, J. Komorowski, and L. Polkowski, "A rough set perspective on data and knowledge," in Handbook of Data Mining and Knowledge Discovery,Oxford: Oxford University Press, 2002, pp. 134–149.
  3. Butalia, A. , Dhore, M. L. and Tewani, G. (2008), Applications of Rough Sets in the Field of Data Mining, Emerging Trends in Engineering and Technology, ICETET '08. First International Conference, 498- 503.
  4. Cem Kaner, and Walter P. Bond , (2004),Software Engineering Metrics:What do they measure and how do we know?, 10th International Software Metrics Symposium.
  5. Everald E. Mills (1988) Software Metrics, Software Engineering Institute, Institute Carnegie Mellon University , Seattle, Washington 98122.
  6. J. Han, X. Hu, and T. -Y. Lin, "Feature subset selection based on relative dependency between attributes," in Proc. of the 4th International Conf. on Rough sets and Current Trend in Computing, Uppsala, 2004, pp. 176–185. .
  7. K. Thankavel and A. Pethalakshmi, "Dimensionality reduction based on rough set theory," A Review, Applied Soft Computing, vol. 9, no. 1, pp. 1–12, 2009.
  8. Kan, Stephen H. , Jerry Parrish, Diane Manlove "In-Process Metrics for Software Testing", IBM ,Systems Journal, Vol 40, No. 1, February 2001.
  9. V. R. Basili and B. R. Perricone, "Software Errors and Complexity," Comm. ACM, vol. 27, pp. 42-52, 1984.
  10. Z. Pawlak, "Rough set approach to knowledge-based decision support," European Journal of Operational Research, vol. 99, no. 1, pp. 48–57, 1997
Index Terms

Computer Science
Information Sciences

Keywords

Quick Reduct Algorithm Rough Set Theory Approach Software Developers Efficiency