On effects of tokens in source code to accuracy of fault-prone module prediction

In the software development, defects affect quality and cost in an adverse way. Therefore, various studies have been proposed defect prediction techniques. Most of current defect prediction approaches use past project data for building prediction models. That is, these approaches are difficult to apply new development projects without past data. In this study, we use 28 versions of 8 projects to conduct experiments using the fault-prone filtering technique. Fault-prone filtering is a method that predicts faults using tokens from source code modules. Since the classes of tokens have impact to the accuracy of fault-proneness, we conduct an experiment to find appropriate token sets for prediction. From the results of experiments, we found that using tokens extracted from all parts of modules is the best way to predict faults and using tokens extracted from code part of modules shows better precision.