Approach for Analyzing Clustering Technique in Software Maintenance for Object Oriented System

Dr. Anil Kumar Malviya*, N. Badal**
* Associate Professor, Department of Computer Science & Engineering, Kamla Nehru Institute of Technology (KNIT), Sultanpur
** Assistant Professor, Department of Computer Science & Engineering, KNIT, U.P, India.
Periodicity:July - September'2010
DOI : https://doi.org/10.26634/jse.5.1.1208

Abstract

Object Oriented Software Engineering has been emerging field for software development process. Although maintenance may be turn out to be easier for Object Oriented System. But it is unlikely that the maintenance burden will be completely disappearing. Still, maintenance consumes a large portion of software development cost. Therefore it is worthwhile to develop Object Oriented System keeping maintainability as a key issue in design phase. This paper examines the role of clustering technique of data mining in maintenance of software system using object oriented metrics. The presented work evaluates the K-means clustering method by applying it to the commercial software system. The experimental work of software maintenance for the sample data is being simulated on Matlab.

Keywords

Data mining, K-means clustering algorithm, Object-Oriented metrics.

How to Cite this Article?

Dr. Anil Kumar Malviya and N. Badal (2010). Approach for Analyzing Clustering Technique in Software Maintenance for Object Oriented System. i-manager’s Journal on Software Engineering, 5(1),40-44. https://doi.org/10.26634/jse.5.1.1208

References

[1]. Pressman, R.S. (2005). “Software Engineering: A Practitioner's Approach”, Sixth Edition, McGraw Hill International Edition 2005.
[2]. Cem Kanev, and Walter P. Bond, (2004). “Software Engineering Metrics: What Do They Measure and How Do th We Know? 10 International Software Metrics Symposium.
[3]. Li. W., and Henry, S. (1993). “Object-Oriented Metrics that Predict Maintainability”, in The Journal of Systems and Software, Vol. 23, pp. 111-122, 1993.
[4]. Tripathi, A.K., and Malviya, A.K. (2000). “Some Observations on Maintainability Metrics for Object Oriented Software”, in Int'l Journal of Information and Computing Science, Vol. 3, No. 2, pp. 52-56, Dec.
[5]. Tripathi, A.K., and Malviya, A.K. (2002). “On Maintainability and Coupling of Object Oriented Software”, in The Journal of the CSI, Vol.32, No. 3, pp.1-6, Sep.
[6]. Malviya, A.K., and Dutta, M. (2004). “Measuring the Maintainability of Object Oriented Systems”, in Int'l Journal of Information & Computing Science, Vol. 7, No. 2, pp. 1- 12, Dec.
[7]. Zhou, Y., and Leung, H. (2007). “Predicting Object- Oriented Software Maintainability using Multivariate Adaptive regression Splines”, in The Journal of Systems and Software, vol. 80, pp. 1349-1361, 2007.
[8]. Anponellis, P., Antorious, D., and others, (2010). ”A Data Mining Methodology For Evaluating Maintainability According to ISO/IEC-9126 Software Engineering- Product”, students. ceid.upatras.gr/………../A Data Mining Methodology for Evaluating Maintainability according to SQM07.. on date 01|01|2010.
[9]. Rousidis, D., and Tjortjis, C. (2005). “Clustering Data Retrieved from Java Source Code to Support Software maintenance: A Case Study”, in the Proc. of the ninth European Conference on Software Maintenance and Reengineering, IEEE.
[10]. Beyer, D., and Noack, A. (2005). “Clustering Software Artifacts based on Frequent Common Changes”, Proceeding of 13 International workshop on Program Comprehension, IEEE.
[11]. Oca, C.M., and Carver, D.L. (2010). “Identification of Data Cohesive Subsystem Using Data Mining Techniques”, www.cse.yorku.ca/ course_archive/2003- 4/F/6431/... / Data Mining.pdf on date 01|10|2010.
[12]. Kanellopoulos, Y., and Others, “K-attractors: A clustering Algorithm for Software Measurement Data th Analysis”, 19 IEEE International Conference on Tools with A.I.
[13]. Elmasri and Navathe, (2005). “Fundamental of th Database System”, 4 edition, Pearson India.
[14]. Chidamber and Kemerer, C.F. (1994). “A Metrics suite for Object Oriented Design” IEEE Transactions on Software Engineering, Vol. 20, No.4, pp. 476-493, 1994.
[15]. Khothari, J., Denton, T., Shokoutandeh, A., Mancoridis, S., and Hassan, A.E. (2006). “Studying the th Evolution of Software System using Change Clusters”, 14 IEEE International conference on Program Comprehension.
[16]. Dhawan, S., and Kumar, R. (2008). “Analysing Performance of Web Based Metrics for Evaluating Reliability and Maintainability of Hyper Media Applications”, Third International Conference on Broad Band Communications, Information Technology and Bio- Media Applications, 2008.
[17]. Tahir, A., and Ahmad, R. (2010). “An AOP-Based Approach for Collecting Software Maintainability Dynamic Metrics”, Second International Conference on Computer Research and Developments, 2010.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.