There is a common belief that developers' behavioral interaction patterns may affect software quality. However, widely used defect prediction metrics such as source code 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.
For example, Lee et al. REF proposed a set of micro-interaction metrics (MIMs) that leverage developers' interaction information combined with source code metrics to predict defects.
Micro interaction metrics for defect prediction
ESEC/FSE '11
Computer Science