## 7 Comparative Analysis
### Analysis of Results  
We performed a statistical analysis of the results using Wilcoxon statistical test [41]. To evaluate the presented approach, we executed the statistical test on accuracy scores from  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Dataset Used** & **Number of Drift Points Detected** \\ \hline
[13]—NB & jm1 & 2 \\ \hline
[13]—DT & jm1 & 2 \\ \hline PL—NB & jm1 & 23 \\ \hline PL—RF & jm1 & 34 \\ \hline
[13]—NB & prop & 5 \\ \hline
[13]—DT & prop & 4 \\ \hline PL—NB & prop & 35 \\ \hline PL—RF & prop & 3 \\ \hline PL—NB & KC1 & 1 \\ \hline PL—RF & KC1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative analysis with the existing CD-detection method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Aim** & **Dataset Used** & **Evaln Measures Used** & **Techn-iques Used** & **Results in terms of No. of Drift Points** \\ \hline
[12] & \begin{tabular}{c} To show that \\ Software \\ Systems \\ are subject \\ to CD \\ \end{tabular} & \begin{tabular}{c} Eclipse, \\ Netbeans, \\ Mozilla, \\ Open Office \\ \end{tabular} & \begin{tabular}{c} ROC-AUC, \\ RMSE, \\ MAE, PC, \\ p-value \\ \end{tabular} & \
\begin{tabular}{c} Weka’s \\ J48 \\ \end{tabular} &
\begin{tabular}{c} Change \\ Detected \\ \end{tabular} \\ \hline
[10] & \begin{tabular}{c} To find \\ existence \\ of CD \\ \end{tabular} & \begin{tabular}{c} Eclipse, \\ Netbeans, \\ Mozilla, \\ Open Office \\ \end{tabular} & \begin{tabular}{c} ROC-AUC, \\ RMSE, \\ MAE, \\ PC, p-value \\ \end{tabular} & \
\begin{tabular}{c} Weka’s \\ J48 \\ \end{tabular} &
\begin{tabular}{c} Detected \\ Changing \\ Periods in \\ Software Projects \\ \end{tabular} \\ \hline
[13] & \begin{tabular}{c} CD \\ Detection \\ \end{tabular} & jm1, & \begin{tabular}{c} Error Rate \\ Based On \\ Trop, TN \\ FP, FN \\ \end{tabular} & \begin{tabular}{c} NB, DT \\ and Chi- \\ Square \\ Test \\ \end{tabular} & \begin{tabular}{c} 1 :-ND(jm1) \\ 1 :-DT(jm1) \\ 4 :-ND(prop) \\ 5 :-DT(prop) \\ \end{tabular} \\ \hline PL & \begin{tabular}{c} CD \\ Detection \\ and \\ Handling \\ \end{tabular} & \begin{tabular}{c} jm1, \\ KC1, \\ prop \\ \end{tabular} & \begin{tabular}{c} Accuracy, \\ ROC-AUC, \\ No of \\ Drift Points \\ \end{tabular} & \
\begin{tabular}{c} NB, RF \\ and \\ PL-NB, \\ PL-RF \\ PL-RF \\ \end{tabular} &
\begin{tabular}{c} 23 :-PL-NB(jm1) \\ 3 :-PL-RF(jm1) \\ 3 :-PL-RF(jm1) \\ 1 :-PL-NB(prop) \\ 2 :-PL-RF(KC1) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison with similar methods.  
the NASA and Jureczko datasets using the base NB and PL-NB, and base RF and PL-RF approaches. These test results, presented in Table 10, showed that the accuracy scores of base and paired learners of the NB and RF classifiers, previously presented in Table 6 in Section 5, are statistically significant.  
The Wilcoxon signed rank test statistic is defined as small W+ (sum of positive ranks) and W\(-\) (sum of the negative ranks). In our experimental results, all of the values of the AUC scores for PL-NB and PL-RF are higher than or equal to the corresponding AUC scores for basic NB and basic RF. Therefore, the Wilcoxon signed rank test resulted in 0 value of the test statistic.