Outlier Removal Based on Third-Party Data in Fault-prone Module Prediction

Abstract

In software development, the ability to predict fault-prone modules, that are likely to contain bugs, with high accuracy leads to more efficient testing and debugging. In order to improve prediction accuracy, removal of outlier data in training data of prediction models that adversely affect prediction has been studied. In this paper, we propose a more robust outlier removal method that identifies and removes outliers in training data using a third-party dataset obtained from projects different from the one being predicted in the cross-version prediction. Results of evaluation experiments show that the proposed method can improve prediction accuracy for the majority of projects and is more effective than existing outlier removal methods such as MOA and CC-MOA.