Grey relational classification algorithm for software fault proneness with SOM clustering

The estimation by the human judgment to deal with the inherent uncertainty of software gives a vague and imprecise solution. To cope with this challenge, we propose a new hybrid analogy model based on the integration of grey relational analysis (GRA) classification with self-organising map (SOM) clustering. In this paper, a new classification approach is proposed to distribute the data to similar groups. The attributes are selected based on GRC values. In the proposed, the similarity measure between reference project and cluster head is computed to determine the cluster to which target project belongs. The fault-proneness of reference project is estimated based on the regression equation of the selected cluster. The proposed algorithm gives resilience to users to select features for both continuous and categorical attributes. In this study, two scenarios based on the integration of proposed classification with regression have been proposed. Experimental results show significant results indicating that proposed methodology can be used for the prediction of faults and produce conceivable results when compared with the results of multilayer-perceptron, logistic regression, bagging, naive Bayes and sequential minimal optimisation (SMO).