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In cross-version defect prediction (CVDP), historical data is derived from the prior version of the same project to predict defects of the current version. Recent studies in CVDP focus on subset selection to deal with the changes of the data distributions. No prior study has focused on training data arriving in streaming fashion across the versions where the significant differences between versions make the prediction unreliable. We refer to this situation as Drift Propensity (DP). By identifying DP, necessary steps can be taken (e.g., updating or retraining the model) to improve the prediction performance. In this paper, we investigate the chronological defect datasets and identify DP in the datasets. The no-memory data management technique is employed to manage the data distributions and a DP detection technique is proposed. The idea behind the proposed DP detection technique is to monitor the algorithm's error-rate …