We have proposed a detection method of fault-prone modules based on the spam filtering technique, ``Fault-prone filtering. Fault-prone filtering is a method that uses the text classifier (spam filter) to classify source code modules in software. In this study we propose a extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer is a good source of fault-prone filtering.