An Intuitionistic Fuzzy K-Medoids Based Similar Pattern Analysis in Software Defect Prediction

International Journal of Computer Trends and Technology (IJCTT)          
© 2018 by IJCTT Journal
Volume-55 Number-1
Year of Publication : 2018
Authors : M. Jaikumar, V. Kathiresan
DOI :  10.14445/22312803/IJCTT-V55P108


M. Jaikumar, V. Kathiresan "An Intuitionistic Fuzzy K-Medoids Based Similar Pattern Analysis in Software Defect Prediction". International Journal of Computer Trends and Technology (IJCTT) V55(1):41-49, January 2018. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
In the software field the quality and the reliability are the important factors which have to be greatly handled with the help of software defect prediction. During the period of development and the maintenance of the software detecting and rectifying the software defects is really more expensive. By designing prediction model which accurately determines the occurrence of defect in software greatly assist in efficient software testing, reducing the cost and considerably improvising testing process of software by focusing on fault prone modules. Machine Learning Clustering has emerged as a way to predict the fault in the software system by grouping the similar patterns. This paper focuses on predicting the software defect contributed by NASA repository dataset. This work uses Intuitionistic fuzzy K-medoids based clustering for finding the similar pattern among the software defect dataset and design the rule based on it.

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Software, defect, intuitionistic fuzzy, K-medoids, clustering, fault