Micro Interaction Metrics for Defect Prediction


There is a common belief that developers' behavioral interaction patterns may affect software quality. However, widely used defect prediction metrics such as software complexity metrics, change churns, and the number of previous defects do not capture developers' direct interactions. We propose 56 novel micro interaction metrics (MIMs) that leverage developers' interaction information stored in the Mylyn data. Mylyn is an Eclipse plug-in, which captures developers' interactions such as file editing and selection events with time spent. To evaluate the performance of MIMs in defect prediction, we build defect prediction (classification and regression) models using MIMs, traditional metrics, and their combinations. Our experimental results show that MIMs significantly improve defect classification and regression accuracy.

Data Sets for Experiments

We provide data sets (arff files for Weka) to reproduce our experimental results. (Also, you can download all datasets in a zip file. datasets.zip) To reproduce experimental results with the above data sets, Please, follow the steps below: * For MIMs, each metric name matched in Appendix defined here. (names of MIMs)


Project Members

If you have any comments/questions regarding the research work or the tool, please feel free to contact any of the project members.