Can Faulty Modules Be Predicted by Warning Messages of Static Code Analyzer?

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 which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an 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 are a good source of fault-prone filtering as the original source code. Moreover, it is discovered that it is more effective than the conventional method (that is, without static code analyzer) to raise the coverage rate of actual faulty modules