# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 4 Experimental Research
### Experimental Results and Analysis  
In order to verify the effectiveness of the MFTCPDP method, we first conducted an experimental study on the MFTCPDP method on two datasets frequently used by researchers, then compared the performance with the benchmark method, several currently popular CPDP methods and WPDP methods. Finally, we considered the internal factors that affect the performance of the MNTCPDP method, including the influence of the method parameters and the choice of a classifier on the method performance. Therefore, we \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Project** & **Modules** & **Features** & **Defects** & **Defect Ratio** \\ \hline Apache & 194 & 26 & 98 & 50\% \\ Safe & 56 & 26 & 22 & 39\% \\ ZXing & 399 & 26 & \(118\) & 30\% \\ EQ & 325 & 61 & 129 & 40\% \\ JDT & 997 & 61 & 206 & 21\% \\ LC & 399 & 61 & 64 & 9\% \\ ML & 1862 & 61 & 245 & 13\% \\ PDE & 1492 & 61 & 209 & 14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental datasets. designed three research questions and realized a comprehensive analysis of the MFTCDP method through the answers to these questions. Question 1: Compared with the benchmark method, can the MFTCDP method improve the performance of CPDP? Additionally, is it better than several popular CPDP methods? How does the performance compare to the WPDP method? [PERSON] et al. [45] and [PERSON] et al. [46] conducted empirical studies on the current reproducible CPDP methods and found that most of the current CPDP methods are difficult to surpass the performance of some existing classical methods, such as the method of reducing distribution difference proposed by [PERSON] et al. [47] evaluated by [PERSON] et al., the Burak filtering method based on instance selection proposed by [PERSON] et al. [14], and the TCA + method based on feature transformation and migration proposed by [PERSON] et al. [19]. These three methods rank high in the comprehensive performance of all CPDP methods; therefore, we compared the MFTCDP method with these three CPDP methods. Moreover, in the CPDP research, [PERSON] et al. [26] proposed a method of directly using source project data to train the model and then predicting the target project, which is recorded as the DCPDP (i.e., direct cross-project defect prediction) method. It is often used as a benchmark method for comparison. In addition, we have also conducted an experimental comparison with the 10-fold cross-validated WPDP method. The 10-fold cross-validated WPDP method is the most reasonable experimental setting, which can show the gap between the MFTCDP method and WPDP and analyze the problems of the current method. Before the performance comparison of the methods, we first conducted experiments to explore the feature distortion phenomenon in the original feature space. Therefore, we carried out the experiment of the DCPDP method with data standardization operation, which was recorded as the comparison between the DCPDP-Norm and the DCPDP method. Generally speaking, a simple data normalization operation can improve the experimental performance, but when the value distribution in the feature space is distorted, the experimental performance may be reduced. We conducted experiments on two datasets and obtained the following experimental results. Table 2 shows the performance comparison between DCPDP-Norm and DCPDP. Table 3 shows the performance comparison between the MFTCDP method and benchmark method DCPDP, several DCPDP methods, and WPDP method. First, we compared DCPDP and DCPDP-Norm with data standardization operations. As shown in Table 2, the overall performance difference between the DCPDP-Norm and the DCPDP on the Relink dataset (i.e., rows 2 to 7) is considerably noticeable. Specifically, it can be found in each pair of CPDP experiments that data standardization operation sometimes cannot improve the performance of the method or even regress the performance.