Abstract: Utilizing a public dataset and a private dataset,we employed the statistics analysis and machine learning methods to empirically investigate the relation between static code metrics and the proneness of software modules.We conclude that fault severity impacts the performance of fault-proneness prediction,and we should take the fault severity into account when choosing appropriate metrics and classification models.We also conclude that the performance of prediction for low severity faults is better than the high severity faults.