Comparison of Outlier Detection Methods in Fault-proneness Models

In this paper, we experimentally evaluated the effect of outlier detection methods to improve the prediction performance of fault-proneness models. Detected outliers were removed from a fit dataset before building a model. In the experiment, we compared three outlier detection methods (Mahalanobis outlier analysis (MOA), local outlier factor method (LOFM) and rule based modeling (RBM)) each applied to three well-known fault-proneness models (linear discriminant analysis (LDA), logistic regression analysis (LRA) and classification tree (CT)). As a result, MOA and RBM improved F1-values of all models (0.04 at minimum, 0.17 at maximum and 0.10 at mean) while improvements by LOFM were relatively small (-0.01 at minimum, 0.04 at maximum and 0.01 at mean).

• Type: Conference
• Year: 2007
• Where: ESEM
• Authors: Shinsuke Matsumoto, Yasutaka Kamei, Akito Monden, Ken-ichi Matsumoto
• Keywords: ESEM 2007, Fault-proneness Models, Outlier Detection Methods, Outlier Factor Method, Software Engineering