Software Defect Classification: A Comparative Study with Rough Hybrid Approaches

摘要

This paper is an extension of our earlier work in combining strengths of rough set theory and neuro-fuzzy decision trees in classifying software defect data. The extension includes the application of a rough-fuzzy classification trees to classifying defects. We compare classification results for five methods: rough sets, neuro-fuzzy decision trees, partial decision trees, rough-neuro-fuzzy decision trees and rough-fuzzy classification trees. The analysis of the results include a paired t-test for accuracy and number of rules. The results demonstrate that there is improvement in classification accuracy with the rough fuzzy classification trees with a minimal set of rules. The contribution of this paper is a comparative study of several hybrid approaches in classifying software defect data.