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Parametric Based Clustering of Reusable Components.

Author(s)
M. H. ZAFAR Department of Computer Science and Information Technology, University of Sargodha, Sargodha, Pakistan
M. ILYAS Department of Computer Science and Information Technology, University of Sargodha, Sargodha, Pakistan
S. RAZZAQ Department of Computer Science and Information Technology, University of Sargodha, Sargodha, Pakistan
F. MAQBOOL Department of Computer Science and Information Technology, University of Sargodha, Sargodha, Pakistan
W. AHMAD Department of Computer Science, University of Engineering and Technology Taxila, Taxila, Pakistan
S. M. ADNAN Department of Computer Science, University of Engineering and Technology Taxila, Taxila, Pakistan
Abstract
Building new software by using existing software that has been developed by using reusability principles is known as software reuse. It results in reduction of effort and time to develop software. It also increases reliability, portability, maintainability and productivity of software product. But the problem is a lack to symmetric way to store reusable components so that retrieval of component done with less time. One of the solutions is to classify reusable components. We use clustering technique to classify reusable components because clustering results in reduction of search space by cataloguing similar objects together. In this research we propose a framework that is used to understand the process of clustering and to give a practical shape to this framework. In this framework software reusable components are provide with their associated parameters. On the basis of these parameters, software reusable components are c lustered. Finally proposed clustering algorithm is evaluated by applying this algorithm on different software reusable components. Presentation of results is in the form of table and graph which shows the successful clustering of reusable components.
Publication Details
Page(s) 419-426
DOI DOI not available
Published Journal: Sindh University Research Journal, Volume: 51, Issue: 3, Year: 2019

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Keywords
Software Clustering
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