## 3 Experimental Results and Analysis
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Company A \(\cap\) Company B** & **NASA \(\cap\) SOFTLAB** & **AEEEM \(\cap\) SOFTLAB** & **NASA \(\cap\) AEEM** \\ \hline Number & 28 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of common features between projects of different companies. performance of GMOTDP is better than other methods, and the AUC average value of the prediction results of the other four methods is improved by 0.1981, 0.2305, 0.1331, and 0.1106, respectively. It can be seen that the prediction effect of GMOTDP is better than the other four methods from the boxplot representation of Figure 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Target** & **TCA+** & **CCA+** & **KCCA+** & **KSETE** & **GMOTDP** \\ \hline \multirow{3}{*}{PC3} & AR3 & 0.6399 & 0.6232 & 0.7689 & 0.7474 & 0.8297 \\  & AR4 & 0.7151 & 0.6462 & 0.7389 & 0.7743 & 0.9014 \\  & AR5 & 0.6249 & 0.6692 & 0.7516 & 0.8242 & 0.9330 \\ \hline \multirow{3}{*}{PC4} & AR3 & 0.6920 & 0.6769 & 0.7310 & 0.7846 & 0.8079 \\  & AR4 & 0.6930 & 0.6462 & 0.7371 & 0.7047 & 0.9252 \\  & AR5 & 0.7214 & 0.6492 & 0.7229 & 0.7728 & 0.8460 \\ \hline \multirow{3}{*}{MW1} & AR3 & 0.6696 & 0.6464 & 0.7089 & 0.6398 & 0.8170 \\  & AR4 & 0.6860 & 0.6923 & 0.8259 & 0.7964 & 0.8682 \\ \cline{1-1}  & AR5 & 0.6429 & 0.5462 & 0.6850 & 0.8286 & 0.9397 \\ \hline \multicolumn{2}{c}{mean} & 0.6761 & 0.6437 & 0.7411 & 0.7636 & 0.8742 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean AUC results for source and target projects with common features using different methods. Figure 4: Results for source and target projects with common features using different methods. Figure 5: Boxplot for source and target projects with common features using different methods. When Tables 4 and 5 are compared, it can be seen that the prediction performance of GMOTDP is not affected by whether there are common features. TCA+, CCA+, and KCCA methods have different degrees of decrease in predictive performance between source and target projects without a common feature. The reason for this situation is that PCA is used to extract the principal components of source and target projects when constructing manifold space, and the reconstructed features can retain most of the relevant information characteristics. GMOTDP does not lose the important information related to defects in data processing, which ensures the effectiveness and universality. The non-parametric test does not assume that the population distribution must conform to the normal distribution. It can infer that the population distribution directly from samples. The Kruskal-Wallis test is carried out under significance level \(\alpha\) = 0.05, and TCA+, CCA+, KCCA+, KSETE, and GMOTDP are compared in pairs. The null hypothesis for each row in Table 6 show that the Method 1 and Method 2 distributions are the same. In order to reveal which of these groups differ from each other, we conduct a post hoc test with the Holm-Bonferroni correction. We use SPSS software to obtain adjusted \(p\)-value, which is directly compared with 0.05, and the difference is considered statistically significant if it is less than 0.05. The last column of the Table 6 clearly shows that there is a significant difference between GMOTDP and TCA+, CCA+, and KCCA+, but there is no significant difference between GMOTDP and KSETE. For the imbalanced datasets, G-mean was used to compare the performance of each prediction method again. G - mean \(=\sqrt{TNR\times Recall}\), \(TNR\) and \(Recall\) denotes specificity and sensitivity of the classifier, respectively. G-mean comprehensive considers the classification performance of minority class and majority class. When the classification accuracy of minority class and majority class is closer, we can get the best G-mean value. It can be found from Tables 7 and 8 that the prediction effect of GMOTDP is better.