# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 4 Experimental Research
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Source\(\rightarrow\)Target** & **MFTCPDP** & **TCA+** & **Watanabe** & **Burak** & **DCPDP** & **WPDP** \\ \hline Safe\(\rightarrow\)Apache & 0.711 & 0.670 & 0.716 & 0.565 & 0.714 & 0.625 \\ Zxing\(\rightarrow\)Apache & 0.653 & 0.671 & 0.705 & 0.358 & 0.645 & 0.625 \\ Zxing\(\rightarrow\)Safe & 0.769 & 0.512 & 0.717 & 0.735 & 0.699 & 0.703 \\ Apache\(\rightarrow\)Jaffe & 0.460 & 0.569 & 0.717 & 0.234 & 0.459 & 0.703 \\ Apache\(\rightarrow\)Zxing & 0.587 & 0.595 & 0.653 & 0.155 & 0.582 & 0.666 \\ Safe\(\rightarrow\)Zxing & 0.652 & 0.628 & 0.636 & 0.596 & 0.654 & 0.666 \\ MEAN & 0.638 & 0.607 & 0.691 & 0.441 & 0.625 & 0.665 \\ JDT\(\rightarrow\)EQ & 0.556 & 0.606 & 0.688 & 0.452 & 0.633 & 0.633 \\ LC\(\rightarrow\)EQ & 0.667 & 0.549 & 0.683 & 0.473 & 0.663 & 0.723 \\ ML\(\rightarrow\)EQ & 0.623 & 0.637 & 0.679 & 0.452 & 0.668 & 0.723 \\ PDE\(\rightarrow\)EQ & 0.628 & 0.608 & 0.687 & 0.453 & 0.674 & \\ LC\(\rightarrow\)JDT & 0.572 & 0.731 & 0.818 & 0.700 & 0.545 & \\ PDE\(\rightarrow\)JDT & 0.724 & 0.743 & 0.827 & 0.702 & 0.571 & 0.829 \\ ML\(\rightarrow\)JDT & 0.608 & 0.726 & 0.828 & 0.701 & 0.578 & 0.829 \\ EQ\(\rightarrow\)JDT & 0.527 & 0.455 & 0.736 & 0.432 & 0.422 & \\ JDT\(\rightarrow\)LC & 0.889 & 0.798 & 0.825 & 0.861 & 0.884 & \\ ML\(\rightarrow\)LC & 0.882 & 0.861 & 0.860 & 0.863 & 0.875 & 0.865 \\ PDE\(\rightarrow\)LC & 0.876 & 0.786 & 0.811 & 0.860 & 0.871 & \\ EQ\(\rightarrow\)LC & 0.842 & 0.479 & 0.015 & 0.808 & 0.813 & \\ JDT\(\rightarrow\)ML & 0.836 & 0.772 & 0.777 & 0.805 & 0.769 & \\ LC\(\rightarrow\)ML & 0.781 & 0.470 & 0.806 & 0.807 & 0.462 & 0.837 \\ PDE\(\rightarrow\)ML & 0.833 & 0.788 & 0.807 & 0.807 & 0.437 & 0.837 \\ EQ\(\rightarrow\)ML & 0.762 & 0.385 & 0.349 & 0.590 & 0.344 & \\ EQ\(\rightarrow\)PDE & 0.718 & 0.619 & 0.760 & 0.525 & 0.687 & \\ JDT\(\rightarrow\)PDE & 0.828 & 0.797 & 0.781 & 0.790 & 0.826 & 0.831 \\ LC\(\rightarrow\)PDE & 0.780 & 0.804 & 0.812 & 0.796 & 0.789 & 0.831 \\ ML\(\rightarrow\)PDE & 0.822 & 0.819 & 0.822 & 0.794 & 0.809 & \\ MEAN & 0.738 & 0.671 & 0.718 & 0.684 & 0.667 & 0.817 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of F1 value between MFTCPDP and several CPDP methods and WPDP. pared with DCPDP, in a total of 26 CPDP experiments on the two datasets, the MFTCPDP method can achieve better prediction performance in most of the cross-project defect prediction experiments. The F1 value of the MFTCPDP method is higher in 20 groups of experiments, and the F1 value of the remaining 6 groups of experiments is higher in the DCPDP method, but the performance gap of these 6 groups of experiments is very weak. For example, in the Safe\(\rightarrow\)ZXing experiment, MFTCPDP obtained an F1 value of 0.652, while DCPDP obtained an F1 value of 0.654, with a difference of only 0.002. From the average value, the performance of MFTCPDP is also better. The DCPDP method achieved an average value of 0.625 on the Relink dataset and an average value of 0.667 on the AEEEM dataset, while the MFTCPDP method obtained an average value of 0.638 on Relink dataset and 0.738 on the AEEEM dataset. In addition, from the distribution of performance values, the minimum F1 value obtained by the DCPDP method in all experiments is 0.344, the maximum F1 value is 0.884, and there are five groups of experiments with a value lower than 0.5, but the minimum F1 value obtained by MFTCPDP method in all experiments is 0.460, the maximum F1 value is 0.889, and only one group of experiments with a value lower than 0.5. This shows that the MFTCPDP method has a smaller F1 value fluctuation range than the DCPDP method, and the overall performance is more stable. Therefore, compared with the benchmark method DCPDP, the MFTCPDP method can improve the performance of CPDP. Compared with several CPDP methods, the MFTCPDP method also has advantages. Compared with the Burak filter method, the MFTCPDP method achieved better performance in all CPDP experiments on the Relink dataset.