A hybrid faulty module prediction using association rule mining and logistic regression analysis

This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key measures of interestingness of an association rule (support, confidence and lift) to select useful rules for the prediction. If a module satisfies the premise (i.e. the condition in the antecedent part) of one of the selected rules, the module is classified by the rule as either faultprone or not. Otherwise, the module is classified by the logistic model. We experimentally evaluated the prediction performance of the proposed method with different thresholds of each rule interestingness measure (support, confidence and lift) using a module set in the Eclipse project, and compared it with three well-known fault-proneness models (logistic regression model, linear discriminant model and classification tree). The result showed that the improvement of the F1-value of the proposed method was 0.163 at maximum compared...

• Type: Conference
• Year: 2008
• Where: ESEM
• Authors: Yasutaka Kamei, Akito Monden, Shuuji Morisaki, Ken-ichi Matsumoto
• Keywords: Association Rule, ESEM 2008, Fault-prone Module Prediction, Logistic Regression, Software Engineering