Revisiting the evaluation of defect prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Towards a software failure cost impact model for the customer: an analysis of an open source product
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Controversy Corner: Preserving knowledge in software projects
Journal of Systems and Software
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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.