Implementing Clustering Based Approach for Evaluation of Success of Software Reuse using K-means algorithm

Jagmeet Kaur,Dr Dheerendra Singh

doi:10.24297/ijct.v4i3.4199

Abstract

A great deal of research over the past several years has been devoted to the development of methodologies to create reusable software components and component libraries. But the issue of how to find the contribution of the factor towards the successfulness of the reuse program is still in the naÃ¯ve stage and very less work is done on the modeling of the success of the reuse. The success and failure factors are the key factors that predict the successful reuse of software. An algorithm has been proposed in which the inputs can be given to K-Means Clustering system in form of tuned values of the Data Factors and the developed model shows the high precision results , which describe the success of software reuse.

Highlights

Software Reuse and Success factors: - Systematic reuse is generally recognized as a key technology for improving software productivity and quality (Mili et al 1995), possibly with a higher payoff than process improvement or process automation(Boehm 1993)
In the April 2002 TSE article Success and Failure Factors in Software Reuse [1], Morisio et al sought key factors that predicted for successful software reuse
False positives (FP) refer to fault-free modules incorrectly labelled as faulty modules

Summary

Introduction

Software Reuse and Success factors: - Systematic reuse is generally recognized as a key technology for improving software productivity and quality (Mili et al 1995), possibly with a higher payoff than process improvement or process automation(Boehm 1993). In the April 2002 TSE article Success and Failure Factors in Software Reuse [1], Morisio et al sought key factors that predicted for successful software reuse Their data came from a set of structured interviews conducted with project managers of 24 European projects from 19 companies in the period 1994 to 1997. Successes were achieved when, given a potential for reuse because of commonality among applications, management committed to introducing reuse processes, modifying non-reuse processes, and addressing human factors While addressing those three issues turned out to be essential, the lower-level details of how to address them varied greatly: for instance, companies produced large-grained or small-grained reusable assets, did or did not perform domain analysis, did or did not use dedicated reuse groups, used specific tools for the repository or no tools. Clustering is usually performed when no information is available concerning the membership of data items to predefined classes

Problem Formulation:

Selection of Dataset and Factors

Perform clustering

Conclusion