Merits of using repository metrics in defect prediction for open source projects

Many corporate code developers are the beta testers of open source software. They continue testing until they are sure that they have a stable version to build their code on. In this respect defect predictors play a critical role to identify defective parts of the software. Performance of a defect predictor is determined by correctly finding defective parts of the software without giving any false alarms. Having high false alarms means testers/developers would inspect bug free code unnecessarily. Therefore in this research we focused on decreasing the false alarm rates by using repository metrics. We conducted experiments on the data sets of Eclipse project. Our results showed that repository metrics decreased the false alarm rates on the average to 23% from 32% corresponding up to 907 less files to inspect.