# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 4 Experimental Research
### Experimental Results and Analysis
However, the performance of other experiments is completely inferior to WPDP. The same is true for the other CPDP methods, and it is difficult to surpass the WPDP method. However, the WPDP method is not suitable for practical applications. Therefore, this is also the focus of current CPDP research. When the target project is completely unmarked, the performance of CPDP should further improve so as to be used in actual production. In order to ensure that our analysis is reasonable, we carried out a statistical test and analysis on the above experimental results. We used Wilcoxon signed-rank test, which is a non-parametric statistical hypothesis test used to judge whether two matched samples are selected from the same distribution. If the \(p\)-value reported by the Wilcoxon signed-rank test is less than 0.05, then the distributions to be tested are considered to be significantly different; otherwise, they are not considered to be significant. We conducted four groups of Wilcoxon signed-rank tests--namely, MFTCPDP and TCA+, MFTCPDP and Burak, MFTCPDP and DCPDP, MFTCPDP and Watanabe, respectively marked as M&T, M&B, M&D, and M&W. Finally, we obtained four \(p\) values--namely, M&T 0.0248, M&B 0.0002, M&D 0.004, and M&W 0.2796. We found that the \(p\) values of M&T, M&B, and M&D are all less than 0.05, which means that there is a significant difference between MFTCPDP and TCA+, Burak, DCPDP. This shows that our above analysis is reasonable. Additionally, compared with the benchmark method DCPDP, the CPDP method Burak filter, and the TCA+, our MFTCPDP method indeed performs better. However, the \(p\) value of M&W is 0.2796, which exceeds 0.05. This shows that there is no significant difference between MFTCPDP and Watanabe. However, this does not mean that our above analysis is unreasonable. In fact, in terms of overall performance, compared with Watanabe, the advantage of MFTCPDP is indeed very weak, but the Watanabe method performed abnormally in some cases mentioned above. Therefore, our MFTCPDP method is more reliable from this point of view. Finally, we explain the execution efficiency of the MFTCPDP algorithm (i.e., Algorithm 1). The 26 groups of experiments shown in Table 3 are all executed by the process shown in Algorithm 1. We executed the algorithm twice. The first time we executed it on the Relink dataset, we obtained six sets of experimental results, which required about 3.2 s; the second time we executed it on the AEEEM dataset, we obtained 20 sets of experimental results, which required about 10.8 s. The average time of each experiment is about 0.5 s. This time efficiency is acceptable. Question 2: How does the setting of different subspace dimensions in the MFTCPDP method affect the performance of the method? In the manifold feature transformation of source project and target project, the selection of subspace dimension entails how many dimensions of data need to be retained to represent the source project and the target project in the manifold space. This parameter may have an impact on the experimental performance. We aimed to analyze whether different dimension settings affect the final experimental performance, and then confirm the optimal subspace dimension. In order to analyze the influence of setting different subspace dimensions on the performance of the MFTCPDP method during the transformation of manifold features, we selected different subspace dimensions on the Relink dataset and the AEEEM dataset to conduct experiments. The software projects on Relink dataset have 26 features; therefore, we selected subspace dimensions from 13 to 1 for experiments. The software projects on the AEEEM dataset have 61 features, and the number of features is relatively large; therefore, we studied the performance in several subspace dimensions of 30, 25, 20, 15, 10, and 5. We made box plots of F1 values corresponding to different subspace dimensions for all CPDP experiments on the Relink dataset and AEEEM dataset, as shown in Figures 2 and 3 below.