An Ensemble Approach of Simple Regression Models to Cross-Project Fault Prediction

In software development, prediction of fault-prone modules is an important challenge for effective software testing. However, high prediction accuracy may not be achieved in cross-project prediction, since there is a large difference in distribution of predictor variables between the base project (for building prediction model) and the target project (for applying prediction model.) In this paper we propose an prediction technique called "an ensemble of simple regression models" to improve the prediction accuracy of cross-project prediction. The proposed method uses weighted sum of outputs of simple (e.g. 1-predictor variable) logistic regression models to improve the generalization ability of logistic models. To evaluate the performance of the proposed method, we conducted 132 combinations of cross-project prediction using datasets of 12 projects from NASA IV&V Facility Metrics Data Program. As a result, the proposed method outperformed conventional logistic regression models in terms of AUC of the Alberg diagram.