Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Choosing software metrics for defect prediction: an investigation on feature selection techniques
Software—Practice & Experience
A General Software Defect-Proneness Prediction Framework
IEEE Transactions on Software Engineering
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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.