1) at the time dimensionality: for multi-version projects, historical data of early releases may not applicable for new release.
2) at the space dimensionality: because of the data lack problem, we need to do cross-project defect prediction in some cases.
We see transfer learning as a potential solution to data shift problem of defect prediction for its capability to learn and predict under different training and test distribution. Our on going experiments support this argument.
First, we observe serious performance reduction on cross-release and cross-project defect prediction (i.e., at the time dimensionality and the space dimensionality ).
Second, after employ a transfer learning framework BDSR (Bias Reduction via Structure Discovery), we do better on cross defect prediction.