# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 4 Experimental Research
### Experimental Results and Analysis
At the same time, MFTCPDP also performed better in average performance. The Burak filter method achieved an average performance value of 0.441, while the MFTCPDP method achieved an average performance value of 0.638. In the 20 groups of CPDP experiments on the AEEEM dataset, the MFTCPDP method performed better in 16 groups. In the other 4 groups of experiments, although the performance was not as high as the Burak filter method, the gap is also very small. For example, in the two experiments of LC\(\rightarrow\)ML and LC\(\rightarrow\)PDE, the performance values of the two are basically around 0.8. From the perspective of performance stability, the Burak filter method has the lowest performance on the Relink dataset of 0.155 and the highest value of 0.735. The lowest performance on the AEEEM dataset is 0.432, the highest value is 0.863, and the performance fluctuates greatly, while the performance of the MFTCPDP method is relatively stable without a significant difference in performance. In fact, the Burak filter method is an instance selection method based on similarity, which simply relies on similarity measurement and destroys the distribution of datasets. Therefore, in many cases, the performance of the Burak filter method fluctuates greatly, and the performance is not ideal. From this perspective, the MFTCPDP method has more advantages. Compared with TCA+, our method MFTCPDP also performs better. In the six sets of experiments on the Relink dataset, three sets of MFTCPDP methods performed better. Among the 20 sets of experiments on the AEEEM dataset, 13 sets of MFTCPDP methods performed better. In terms of average value, the MFTCPDP method achieved the average performance of 0.638 and 0.738, respectively, while TCA+ only obtained average performance of 0.607 and 0.671. In general, the MFTCPDP method is indeed better than TCA+. As we discussed earlier, feature distortion is easy to occur when there are many features in the dataset, and the TCA+ method is to map the data of the source project and the target project into a potential space to reduce the distribution difference between the source project and the target project. In the case of feature distortion, TCA+ may not be able to map to the latent space while preserving the structure and features of the original dataset. Therefore, the performance is not ideal in many cases. Compared with the Watanabe method, the MFTCPDP method also has advantages, but the advantages are relatively weak. In terms of average performance, on the Relink dataset, the MFTCPDP method achieved an average performance of 0.638, while the Watanabe method achieved an average performance of 0.691. On the AEEEM dataset, the average performance of the MFTCPDP method reached 0.738, while the Watanabe method achieved an average performance of 0.718. From this point of view, there seems to be little difference. However, the Watanabe method performs abnormally in some cases. For example, the F1 value of EQ\(\rightarrow\)LC is only 0.015, and the F1 value of EQ\(\rightarrow\)ML is only 0.349. We analyzed that the Watanabe method relies on the mean value of the attribute to reduce the distribution difference of the data. Sometimes, the value of some attributes is special, such as when the value is all 0, the average value of the attribute is 0, etc., resulting in poor performance in some cases. Therefore, some feature selection methods need to be combined with the Watanabe method to achieve better performance. Finally, we compared the performance of the MFTCPDP method and the 10-fold cross-validated WPDP. Since WPDP uses part of the project data to train the model to predict the remaining part of the data, only one F1 value was generated for each project. It can be inferred from Table 3 that our MFTCPDP method has a large gap, compared with WPDP. Only in the experiment with Apache as the target project, the performance of MFTCPDP completely exceeds WPDP. Additionally, in the experiment with LC as the target project, three groups of experiments of MFTCPDP surpass WPDP.