## 4 Experiments  
### Benchmark Datasets  
Datasets we choose in this research are software defect datasets from Marian Jureczko Datasets [53], NASA MDP datasets from [PERSON] [54] and Eclipse bug datasets from [PERSON] [55] as benchmark data. Datasets in the first two research can be obtained from the website ([[https://zenodo.org/search?page=1&size=20&q=software%20](https://zenodo.org/search?page=1&size=20&q=software%20) defect%20 predictio]([https://zenodo.org/search?page=1&size=20&q=software%20](https://zenodo.org/search?page=1&size=20&q=software%20) defect%20 predictio)) and the Eclipse bug datasets are downloaded from Eclipse bug repository ([[https://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/](https://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/)]([https://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/](https://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/))). We investigate the sample size and IR value of each set (totally 108), and statistical results are shown in pie graph in Figure 3. Two basic distribution characteristics are listed as follows:  
1. Most IR (imbalanced ratio) of SDP datasets range from 2 to 100.
2. The sample size of SDP datasets in different projects has a huge disparity. Some small datasets are less than 100, while some large datasets are more than 10,000.  
In experiments, we choose 29 datasets from SDP datasets we investigated above for the following experiments as benchmark data. The information of selected datasets are presented in Table 1. To solve binary classification problem, we regard the samples of which the label is the number of bugs and greater than 1 as the same class and redefine the label as \"1\", meanwhile, the samples with the \"0\" label are not changed.  
The definition of imbalanced ratio is the ratio of number of negative samples to number of positive samples, and the sample size is the number of samples of a dataset.  
\[IR=\frac{number\ of\ negative\ samples}{number\ of\ positive\ samples} \tag{7}\]  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Abbreviation** & **Feature** & **Sample Size** & **Label = 1** & **Label = 0** & **IR** \\ \hline synapse-1.2 & synapse-1.2 & 20 & 256 & 86 & 170 & 1.977 \\ jedit-3.2 & jedit-3.2 & 20 & 272 & 90 & 182 & 2.022 \\ synapse-1.1 & synapse-1.1 & 20 & 222 & 60 & 162 & 2.700 \\ log4j-1.0 & log4j-1.0 & 20 & 135 & 34 & 101 & 2.971 \\ jedit-4.0 & jedit-4.0 & 20 & 306 & 75 & 231 & 3.080 \\ ant-1.7 & ant-1.7 & 20 & 745 & 166 & 579 & 3.488 \\ camel-1.6 & camel-1.6 & 20 & 965 & 188 & 777 & 4.133 \\ camel-1.4 & camel-1.4 & 20 & 872 & 145 & 727 & 5.014 \\ Xerces-1.3 & Xerces-1.3 & 20 & 453 & 69 & 384 & 5.565 \\ Xalan-2.4 & Xalan-2.4 & 20 & 723 & 110 & 613 & 5.573 \\ jedit-4.2 & jedit-4.2 & 20 & 367 & 48 & 319 & 6.646 \\ arc & arc & 20 & 234 & 27 & 207 & 7.667 \\ synapse-1.0 & synapse-1.0 & 20 & 157 & 16 & 141 & 8.813 \\ tomcat & tomcat & 20 & 858 & 77 & 781 & 10.143 \\ camel-1.0 & camel-1.0 & 20 & 339 & 13 & 326 & 25.077 \\ PC5 & PC5 & 38 & 1694 & 458 & 1236 & 2.699 \\ KC1 & KC1 & 21 & 1162 & 294 & 868 & 2.952 \\ JM1 & JM1 & 21 & 7720 & 1612 & 6108 & 3.789 \\ prop-5 & prop-5 & 20 & 8516 & 1299 & 7217 & 5.556 \\ prop-1 & prop-1 & 20 & 18,471 & 2738 & 15,733 & 5.746 \\ eclipse-metrics-files-3.0 & ec -3.0 & 198 & 10,593 & 1568 & 9025 & 5.7557 \\ eclipse-metrics-files-2.0 & ec2.0 & 198 & 6729 & 975 & 5754 & 5.902 \\ PC4 & PC4 & 37 & 1270 & 176 & 1094 & 6.216 \\ PC3 & PC3 & 37 & 1053 & 130 & 923 & 7.100 \\ prop-3 & prop-3 & 20 & 10,274 & 1180 & 9094 & 7.707 \\ eclipse-metrics-files-2.1 & ec -2.1 & 198 & 7888 & 854 & 7034 & 8.237 \\ prop-2 & prop-2 & 20 & 23,014 & 2431 & 20,583 & 8.467 \\ prop-4 & prop-4 & 20 & 8718 & 840 & 7878 & 9.379 \\ MC1 & MC1 & 38 & 1952 & 36 & 1916 & 53.222 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of software defect datasets.