# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 4 Experiments
### Analysis of Results
#### 4.3.1 Experiment on Different Methods  
In order to verify the superiority of the DM-CPDP method, we compare it with traditional CPDP models. In this part, we conduct experiments on NASA to NASA and NASA to SOFTLAB respectively. Among many traditional CPDP methods, this paper chooses three classical comparison methods, such as TNB, MSTrA, and HYDRA method. In each experiment, 90% of the source data set and target set are selected randomly as the training set, and the data of remaining target set are used as the test set. Each experiment was repeated 10 times, and the average values of these experiments are obtained from the results. The experimental details of each method are as follows:  
TNB: randomly selects a dataset as the training set, and assigns weights to the training samples according to the target set using data gravity. Finally, the defect prediction model is built using the NB algorithm. MSTrA: selects multiple data sets as training set and distributes weights for each training data according to the target set using data gravitation. In each iteration, each source domain data set is matched with the target set to train weak classifiers, and the weak classifier in the current iteration with the lowest error rate on the target set is selected. HYDRA: merges each candidate source and target dataset as the training set to construct multiple base-classifiers, and then use the genetic algorithm to find the optimal combination of base-classifiers. After that, the process which is similar to the AdaBoost algorithm is used to obtain a linear combination of GA models. The results are shown in Tables 4-6, bold numbers indicate optimal results on a dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Set** & **TNB** & **MSTrA** & **HYDRA** & **DM-CPDP** \\ \hline \multicolumn{5}{c}{NASA to NASA} \\ \hline CM1 & 0.293 & 0.562 & **0.665** & 0.594 \\ PC1 & 0.159 & 0.455 & 0.409 & **0.635** \\ PC2 & 0.237 & 0.172 & 0.293 & **0.368** \\ PC3 & 0.204 & 0.464 & **0.801** & 0.776 \\ PC4 & 0.198 & 0.352 & **0.490** & 0.468 \\ PC5 & 0.323 & 0.517 & 0.607 & **0.647** \\ KC1 & 0.467 & 0.585 & 0.769 & **0.832** \\ KC2 & 0.566 & 0.571 & **0.905** & 0.684 \\ KC3 & 0.258 & **0.537** & 0.454 & 0.450 \\ KC4 & 0.307 & 0.273 & 0.479 & **0.502** \\ MC1 & 0.332 & 0.446 & 0.681 & **0.755** \\ MC2 & 0.527 & 0.409 & 0.514 & **0.597** \\ MW1 & 0.213 & 0.282 & 0.227 & **0.393** \\ JM1 & 0.396 & 0.563 & 0.595 & **0.811** \\ \hline \multicolumn{5}{c}{average} \\ \hline \multicolumn{5}{c}{NASA to SOFTLAB} \\ \hline ar3 & 0.416 & 0.708 & 0.755 & **0.781** \\ ar4 & 0.509 & 0.714 & 0.713 & **0.753** \\ ar5 & 0.652 & 0.809 & 0.846 & **0.863** \\ \hline \multicolumn{5}{c}{average} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of performance indicators F-measure. It can be seen that the DM-CPDP method outperforms several existing algorithms on most data sets. In the NASA to SOFTLAB experiments, the performance of each algorithm on SOFTLAB data sets is stable. DM-CPDP method is better than the other algorithms. The average value of F-measure and AUC are 2.8%-27.3% and 1.7%-7.8% respectively, which is higher than other algorithm. In addition,