Empirical studies on feature selection for software fault prediction

Classification based software fault prediction methods aim to classify the modules into either fault-prone or non-fault-prone. Feature selection is a preprocess step used to improve the data quality. However most of previous research mainly focus on feature relevance analysis, there is little work focusing on feature redundancy analysis. Therefore we propose a two-stage framework for feature selection to solve this issue. In particular, during the feature relevance phase, we adopt three different relevance measures to obtain the relevant feature subset. Then during the feature redundancy analysis phase, we use a cluster-based method to eliminate redundant features. To verify the effectiveness of our proposed framework, we choose typical real-world software projects, including Eclipse projects and NASA software project KC1. Final empirical result shows the effectiveness of our proposed framework.