Machine-learning approaches have been widely used for fault-proneness detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spam-filtering technique. To treat our approach as the conventional fault-prone approaches, we summarize the output of spam-filtering based approach as a metric. In this paper, we show the effectiveness of our new metric comparing the conventional software metrics using two open source projects.