Software Defect Prediction Using Exception Handling Call Graphs: A Case Study

The ability to predict which modules are fault-prone can assist in directing quality enhancement efforts to modules at risk. This is especially critical for high-assurance systems where software failures may have severe consequences. While the field of defect rediction based on software metrics is mature, no study has addressed the use of metrics for predicting faults related to exception handling. In this paper, we propose exception-based software metrics that are based on the structural attributes of exception handling call graphs. We empirically validate the proposed metrics through a case study of Hadoop Core using data mined from software repositories and defect reports. The results of our case study are comparable to the results of other software metric studies in the literature. We also show that our exception-based software metrics can be better predictors of fault-proneness of exception classes compared to conventional software metrics.