## 5 Experimental Settings
### Performance Evaluation Measures  
AUC-ROC: We used the area under the ROC curve to compare the performance of the proposed approach. The accuracies of the PL-NB and PL-RF are computed based on the true and false positive and negative values of prediction. Additionally, the number of misclassifications by the stable learner in the window where the corresponding reactive learner made correct classifications along with a user specified threshold value is used to detect CD. Accuracy alone is not used for drift detection to avoid mis-detection in scenarios where the stable learner's predictions are correct and reactive learner's predictions are wrong for the first half of the window whereas the reactive learner's predictions are correct and stable learner's predictions are wrong for the next half of the window. In such a scenario, as the reactive learner's accuracy increases and the stable learner's accuracy decreases because of a changing concept, the accuracies of both the stable learner and the reactive learner are 50%. Hence, the accuracy measure could not detect the changing concept in such a situation. To avoid such misclassifications, the second proposed measure, i.e., the difference in misclassifications of two learners, is used to detect CD. The area under the ROC curve corresponds to the confidence in positive classifications. As none of the true positive and false positive rates consider the number of true negatives, the classifier performance is not impacted by the skewed class distribution. Therefore, we chose to use these rates to evaluate the performance of the classification in spite of the accuracy measure, which was the same method employed by [PERSON] et al. in [1]. \[TPR=\frac{TruePositives}{TotalPositives},\]  
\[FPR=\frac{FalsePositives}{TotalNegatives}\]  
Cost-Benefit Analysis: To determine the cost-effectiveness of SDP models, a cost-benefit analysis of the employed CD detection and adaptation technique is undertaken. In the context of SDP, [PERSON] first developed the concept of a cost-benefit analysis [34]. In another real-time application for IoT devices, [PERSON] et al. [35] proposed a dynamic cost model to minimize the energy consumption and task execution time to meet the restricted QoS requirements and battery limitations. Using the outputs of SDP models in conjunction with the software-testing process in the software development life cycle, this analysis estimates the amount of testing effort and cost that may be saved. To estimate the defect removal cost of a certain defect prediction model, the analysis model considers the defect removal cost and the defect identification efficiency of different testing phases generated from case studies of different software organizations. [PERSON] et al. [36] looked into how a cost-benefit analysis could be used in SDP. We used the model offered in our study to assess the cost-effectiveness of the CD-detection and -adaptation approach. When defect-prediction results are combined with the software-testing process, Equation (1) provides the estimated defect-elimination cost (Ec). The Ec value is computed on the basis of the base model results. After executing the proposed CD-detection and -adaptation approach, another E'c value is computed using the same equation. The normalized defect-removal cost after the application of the CD-adaptation approach and its interpretation is shown in Equation (2). The phenomenon of normalization has been previously presented by [PERSON] et al. [37] for the computation of normalized RMSE in execution of the hybrid control method for exoskeletons.