Evaluating Defect Prediction Models for a Large Evolving Software System

A plethora of defect prediction models has been proposed and empirically evaluated, often using standard classification performance measures. In this paper, we explore defect prediction models for a large, multi-release software system from the telecommunications domain. A history of roughly 3 years is analyzed to extract process and static code metrics that are used to build several defect prediction models with Random Forests. The performance of the resulting models is comparable to previously published work. Furthermore, we develop a new evaluation measure based on the comparison to an optimal model.