Behavioral-based kernel neutrosophic clustering for heterogeneous cross-project defect prediction

N. Kalaivani; R. Beena

Author(s)

N. Kalaivani Department of Computer Science, Kongunadu Arts and Science College,Coimbatore, Tamil Nadu,India

R. Beena Department of Information Technology, Sri Ramakrishna College of Arts and Science,Coimbatore, Tamil Nadu,India

Abstract

Software defect prediction is essential in software development and maintenance, a highly demanded quality of service. Heterogeneous defect prediction is the most appropriate method for real-time datasets. The heterogeneous metrics of cross-projects are used to predict in many existing models, but the presence of outlier and noisy datasets is not considered an essential factor; thus, the standard prediction models face challenges in producing more accurate results. This paper focuses on handling the impreciseness and vagueness in treating noisy and outliers in software defect prediction datasets. This is accomplished by adapting bipartite ranking-based feature ranking, which converts the target attribute size same as the source attribute size and the feature selection by selecting the top attributes. The noisy and outlier is handled by kernel neutrosophic clustering by introducing the degree of truthiness, indeterminacy and falsity. Finally, Grey Wolf Optimization enhances the heterogeneous cross-project prediction process by selecting the significant centroids in kernel neutrosophic clustering unlabeled instances. This work used six different heterogeneous datasets for software defect prediction, and the results explore that the proposed model performs better and prominently increases the prediction rate.

Publication Details

Page(s) 571-577

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 17, Issue: 5, Year: 2022

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Keywords

kernel neutrosophic clustering, Grey Wolf Optimization, heterogeneous cross-project, Defect Prediction, bipartite ranking

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