# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 4 Experimental Research
### Experimental Results and Analysis
In the figures, the horizontal axis represents the observed indicator and the selected feature dimension, and the vertical axis represents the corresponding indicator value. For example, F1_13 in Figure 2 represents the F1 indicator value with 13 subspace dimensions of the Relink dataset. As can be observed from Figure 2, with the decrease in the subspace dimension on the Relink dataset, the F1 value obtained by the MFTCPDP method gradually increases, and the maximum value of the F1 indicator and the overall value distribution slowly increase. It reaches the maximum value when the subspace dimension is 2, and the performance suddenly decreases when the subspace dimension is 1. The minimum values of each group of experiments are very close, and they are all at the lowest point of the box plot. Therefore, we deduce that on the Relink dataset, the selection of the subspace dimension has a certain impact on the experimental performance, and the performance is best when the subspace  
Figure 3: F1 values obtained from different subspace dimensions on the AEEEM dataset. Figure 2: F1 values obtained from different subspace dimensions on the Relink dataset. dimension is 2. It can be observed from Figure 3 that the performance of the F1 value on the AEEEM dataset is similar to that of Figure 2. Basically, as the subspace dimension decreases, the F1 value obtained by the MFTCPDP method gradually increases, but the improvement here is more obvious. Both the minimum value and overall value distribution are improved, and the performance decreases when the last subspace dimension is 5. Therefore, we also deduce that on the AEEEM dataset, the selection of the subspace dimension has a certain impact on the experimental performance, and the performance is best when the subspace dimension is 10. Analyzing the above two figures, we conclude that the selection of the subspace dimension in the manifold feature transformation will have a certain impact on the experimental performance of the method. Choosing an appropriate subspace dimension can achieve better experimental performance. Question 3: How do different classification models affect the performance of the MFTCPDP method? Any method requires a classifier to complete the construction of the defect prediction model, and the MFTCPDP method is no exception. In the field of software defect prediction, different types of classifiers may have uncertain effects on the final experimental performance. Therefore, we need to study the impact of different classifiers on the performance of the MFTCPDP method. In response to this problem, we selected several different types of classification models. We compared the experimental results of decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), and naive Bayes (NB). We made the F1 values of all cross-project defect prediction experiments on the Relink dataset and the AEEEM dataset into box plots. Figures 4 and 5 show the F1 values obtained by the MFTCPDP method using different classifiers. The horizontal axis in the figures represents the classifiers and indicators used. For example, KNN_F1 represents the F1 value obtained by the MFTCPDP method using the KNN classifier in the experiment, and the vertical axis represents the corresponding F1 value. As can be observed from Figure 4, in the Relink dataset, the F1 values of all classifiers are not significantly different, and the performance of the naive Bayes classifier is slightly better. Figure 5 reveals that in the AEEEM dataset,  
Through the above analysis, we can draw the conclusion that among the several classification models experimented with the MFTCPDP method, the overall performance of the naive Bayes classifier is relatively better. Different types of classification models will have a certain impact on the performance of the MFTCPDP method. Choosing a suitable model for different methods can improve the performance of CPDP.