FECAR: A Feature Selection Framework for Software Defect Prediction

Abstract

Software defect prediction can classify new software entities into either buggy or clean. However the effectiveness of existing methods is influenced by irrelevant and redundant features. In this paper, we propose a new feature selection framework FECAR using Feature Clustering And feature Ranking. This framework firstly partitions original features into k clusters based on FF-Correlation measure. Then it selects relevant features from each cluster based on FC-Relevance measure. In empirical study, we choose Symmetric Uncertainty as FF-Correlation measure, and choose Information Gain, Chi-Square, and Relief as three different FC-Relevance measures. Based on some real projects Eclipse and NASA, we implemented our framework and performed empirical studies to investigate the redundancy rate and the performance of the trained defect predictors. Final results verify the effectiveness of our proposed framework and further provide a guideline for achieving cost-effective feature selection when using our framework.