## 3 Experimental Results and Analysis
### Experimental Results  
Experiments used the Bob under Linux as the backend. Python the multi-paradigm programming language with rich data science packages has been selected. The information of hardware is CPU: Intel(r) Core(tm) i7-9750H, Video card: NVIDIA Geforce RTX 2060. In order to investigate the performance of the proposed algorithm in this paper, GMOTDP is compared with the existing state-of-the-art defect prediction methods, such as TCA+ [23], CCA+ [8], KCAA+ [13], and KSETE [16]. TCA+ and CCA+ are benchmark comparison methods for heterogeneous defect prediction. KCAA+ and KSETE are new heterogeneous defect prediction methods in 2017 and 2019. All methods use logistic regression as the classifier. In the experiments, one project was selected as the target project, and the projects in different datasets were used as the source project for heterogeneous prediction. The area under the working characteristic curve (AUC) of the subjects was used as the evaluation index of the prediction model. The value of AUC ranged from 0 to 1, which is larger, the classification effect of the model is better. For example, six forecasting cases can be carried out for a given SOFTLAB database, when AR3 is selected as the target project. They are called PC3 \(\geq\) AR3, PC4 \(\geq\) AR3, MW1 \(\geq\) AR3, EQ \(\geq\) AR3, JDT \(\geq\) AR3, LC \(\geq\) AR3. Because GMOTDP involves randomness when dealing with class imbalance and feature selection, the average results of 50 repeated experiments were counted for each case to reduce the influence of randomness on the experimental results. The number of common features between projects of different companies is shown in Table 3. In this paper, two sets of experiments are designed to verify the predictive performance of GMOTDP, which is not affected by whether the source and target projects have common features or not. When there are common features between two projects, the projects of NASA and SOFTLAB are used as the source projects and the target projects, respectively, for comparison experiments. When two projects have not common features, the projects of AEEEM and SOFTLAB are used as the source projects and the target projects, respectively, for comparison experiments. Table 4 shows the AUC values of GMOTDP compared with the other four methods when the source and target projects have common features. We selected datasets from NASA and SOFTLAB as source and target projects, respectively. Figure 4 graphically displays Table 4 for a more intuitive display and comparison of the predicted results. It can be seen from Figure 4 and Table 4 that the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Company** & **Project** & **Language** & **Description** & **Metrics** & **Instance** & **Defective (\%)** \\ \hline \multirow{4}{*}{NASA} & PC3 & C & Flight Software for Each & \multirow{4}{*}{37} & \multirow{4}{*}{1077} & \multirow{4}{*}{134 (12.44\%)} \\  & PC4 & C & Flight Software for Each & \multirow{4}{*}{37} & \multirow{4}{*}{1458} & \multirow{4}{*}{178 (12.21\%)} \\  & & C & Orbiting Satellite & & & \\  & MW1 & C & A Zero Gravity Experiment & \multirow{4}{*}{37} & \multirow{4}{*}{253} & \multirow{4}{*}{27 (10.67\%)} \\  & & & about Combustion & & & \\ \hline \multirow{3}{*}{AEEEM} & EQ & Java & OSGi Framework & 61 & 324 & 129 (39.81\%) \\  & JDT & Java & IDE Development & 61 & 997 & 206 (20.66\%) \\  & LC & Java & Text Search Engine Library & 61 & 691 & 64 (9.26\%) \\ \hline \multirow{3}{*}{SOFTLAB} & AR3 & C & Embedded Controller of The & \multirow{3}{*}{29} & \multirow{3}{*}{63} & \multirow{3}{*}{8 (12.7\%)} \\  & & & Washing Machine & & & \\ \cline{1-1}  & AR4 & C & Embedded Controller of The & \multirow{3}{*}{29} & \multirow{3}{*}{107} & \multirow{3}{*}{20 (18.69\%)} \\ \cline{1-1}  & AR5 & C & & Refrigerator & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of projects used in the comparison experiments.