# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 6 Conclusions  
The contribution of this paper is to put forward the method of establishing the cross-project defect prediction module in the dissimilarity space, which provides a new research idea for CPDP. The basic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Set** & \multicolumn{2}{c}{**Method1**} & \multicolumn{2}{c}{**DM-CPDP**} \\ \hline  & **F-Measure** & **AUC** & **Cost-Effectiveness** & **F-Measure** & **AUC** & **Cost-Effectiveness** \\ \hline \multicolumn{5}{c}{NASA to NASA} \\ \hline CM1 & 0.591 & **0.784** & 0.047 & **0.594** & 0.782 & **0.039** \\ PC1 & 0.627 & 0.799 & 0.035 & **0.635** & **0.810** & **0.027** \\ PC2 & 0.229 & 0.558 & 0.001 & **0.368** & **0.563** & **0.001** \\ PC3 & 0.770 & 0.830 & 0.028 & **0.776** & **0.842** & 0.022 \\ PC4 & 0.369 & 0.724 & 0.070 & **0.468** & **0.735** & 0.061 \\ PC5 & **0.648** & 0.762 & 0.062 & 0.647 & **0.768** & **0.057** \\ KC1 & 0.801 & 0.879 & 0.054 & **0.832** & **0.892** & **0.047** \\ KC2 & 0.682 & 0.837 & 0.043 & **0.684** & **0.853** & **0.035** \\ KC3 & 0.346 & 0.681 & 0.096 & 0.450 & **0.685** & 0.084 \\ KC4 & **0.504** & **0.802** & **0.139** & 0.502 & 0.793 & 0.142 \\ MC1 & **0.768** & 0.814 & 0.037 & 0.755 & **0.836** & **0.039** \\ MC2 & 0.582 & 0.645 & 0.132 & **0.597** & **0.662** & 0.119 \\ MW1 & 0.371 & 0.715 & 0.085 & **0.393** & **0.715** & **0.076** \\ JM1 & 0.788 & 0.827 & 0.044 & **0.811** & **0.839** & **0.036** \\ \hline average & 0.577 & 0.761 & 0.062 & **0.608** & **0.770** & **0.056** \\ \hline \multicolumn{5}{c}{NASA to SOFTLAB} \\ \hline ar3 & 0.765 & 0.779 & 0.043 & **0.781** & **0.790** & **0.040** \\ ar4 & 0.724 & 0.782 & 0.051 & **0.753** & **0.837** & **0.042** \\ ar5 & 0.848 & 0.869 & 0.033 & **0.863** & **0.885** & **0.031** \\ \hline average & 0.779 & 0.810 & 0.042 & **0.799** & **0.837** & **0.038** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Different dataset selection method.  
idea of this paper is to use the density-based clustering method to automatically select the cluster centers from the target set as the prototype set. Then the arc-cosine kernel is used to calculate the dissimilarity between the prototype set and the source domain sample as well as the target sample to form the dissimilarity space. After that the training data set are selected by the EMD method, which calculates the cost of converting the data distribution in the source domain to the same data distribution as the target set, and the corresponding samples with a small cost are selected as the training set in the dissimilarity space. Finally, the prediction model is established by TrAdaBoost algorithm.  
In the whole data processing process, we complete two transferring. The first transferring is the dissimilarity measure between samples. In the dissimilarity space, each attribute of each sample is the pairwise dissimilarity between the source domain sample and the prototype sample, and the source domain samples with high correlation to the prototype set have higher measurement values. This method is more flexible than the traditional method of assigning weights to the samples in the feature space. The second transferring is the selection stage of the datasets. In this stage, we make full use of the representation method of samples in the dissimilarity space and use the EMD method to measure the similarity between the source domain dataset and the prototype set. Experiments show that the DM-CPDP method has better prediction performance than the traditional methods.  
[PERSON] conceived the idea; [PERSON], [PERSON] performed the experiments and analyzed the results; [PERSON], [PERSON] wrote the initial manuscript; [PERSON], [PERSON], [PERSON], [PERSON] revised the manuscript together.  
This research was funded by the Central South University Graduate Research Innovation Project under Grant 2018 zzts608.  
The authors declare no conflicts of interest.