## 3 Related Work  
A few investigations revealing assessments of CD in SDP are found in the literature. Below, we talk about these works, which are summarized in Table 1.  
[PERSON] et al. [8] evaluated various fault-prediction research studies from 1993 onwards. They classified the revision of these studies into three groups: data-quality issues, fault-prediction techniques, and software metrics. They found that most fault-prediction studies experimented eth publicly available, object-oriented and process metrics data. They also found that the performance of fault prediction models vary with datasets used for prediction.  
[PERSON] et al. [7] reviewed the prediction performance of thirty-six defect-prediction techniques published during 2000 to 2010 in software engineering. The major contributions of that study were centered on the classification of techniques on the grounds of a prediction model, software metrics, the dependency of variables, and the prediction performance of SDP. They proved that prediction methods using naive Bayes and linear regression (LR) produced better defect predictions than C4.5 and support vector machine. The prediction results for object-oriented software metrics were found to be better than other LOC or complexity metrics. On the other hand, they did not provide any taxonomical classification of SDP models.  
These exhaustive review studies on SDP showed that the SDP models are trained using the historical metrics data. Thereafter, these models use this learned experience to predict the number of faults or the presence/absence of faults in the currently used software modules. This provided the basis for diagnosing the effects of changing data distributions in SDP processes. Only a few studies in this domain have been presented so far, as reviewed below:  
[PERSON] et al. (2019, 2020) were the first to explore the idea of CD in SDP and provided the basis for predicting the occurrence of CD in these studies and for inspecting and evaluating the causes of CD in prediction performance. The authors in those studies determined that the accuracy of prediction changes when chronological datasets are used for training a prediction model. They attributed the altering outputs to changes in the data characteristics. Using an analysis of the files and the defect-fixing procedure of software programs, the authors noticed that changes made to software files tend to drift the concept in the distribution of data, accordingly affecting the outcome of the defect prediction. They evaluated their model on CVS and bugzilla data from four open-source projects: Mozilla, Eclipse, Open Office, and Netbeans. They discovered that variations in the bug-fixating process and the coders responsible for editing the implementation codes are mainly responsible for changes in the quality of the prediction.  
[PERSON] et al. (2020) used a CD-detection method proposed by [PERSON] et al. to discover CD in defect datasets. They applied the Chi-square statistical test with Yates continuity correction to detect CD. They selected two windows for data analysis, recent and overall, through Naive Bayes (NB) and Decision Tree (DT) classifiers and used the accuracy measure to detect drifting points in the data.  
A recent study by [PERSON] et al. (2020) investigated the impact of CD in SDP. They used defect datasets from five open-source projects and showed that the performance of prediction models is not stable over time when using historical datasets for training the static predictor. They suggested designing a tool to detect the CD before training.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
The research in these studies did not provide any solution for handling concept drift in SDP, and they did not explore paired learner for CD adaptation in SDP. Therefore, in our current work, we provide the solution to overcoming the listed shortcomings. We proposed a dynamic approach for detecting CD in SDP. Thereafter, we perform a comprehensive comparative analysis of the results using the Wilcoxon statistical test.