# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 5 Experimental Results
### RQ1: How Effective Is CIB? How Much Improvement Can It Obtain Compared with the Baselines?
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Our CIB**} & \multicolumn{2}{c}{**MAHAKIL**} & \multirow{2}{*}{**AdaC2 [45]**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{2}{c}{**RUS [35]**} & \multicolumn{1}{l}{**None**} \\  & & [44] & & & [46] & & [40] & **RUS [35]** & **None** \\ \hline CM1 & 18.4\(\pm\)5.6 \(\checkmark\) & 16.9\(\pm\)5.9 & **12.6\(\pm\)6.7** & **13.7\(\pm\)6.4** & **19.4\(\pm\)5.7** & 17.3\(\pm\)5.6 \(\checkmark\) & 12.7\(\pm\)7.7** \\ KC1 & 25.5\(\pm\)2.7 \(\checkmark\) & **20.4\(\pm\)2.6** & **25.6\(\pm\)**2.0 & 24.7\(\pm\)2.4 \(\checkmark\) & 20.9\(\pm\)2.7** & 17.7\(\pm\)2.7 & 21.7\(\pm\)2.5 \\ KC3 & **28.7\(\pm\)**6.1 & 27.0\(\pm\)6.9 \(\checkmark\) & **23.1\(\pm\)8.4** & **23.0\(\pm\)6.8** & 26.7\(\pm\)5.2 \(\checkmark\) & 23.2\(\pm\)8.1 & 25.4\(\pm\)8.5 \(\checkmark\) \\ MC1 & 22.9\(\pm\)4.8 & **15.8\(\pm\)5.1** & 43.5\(\pm\)5.9 \(\checkmark\) & **44.0\(\pm\)**7.0 & **11.0\(\pm\)4.0** & **12.8\(\pm\)2.4** & **13.4\(\pm\)7.7** \\ MC2 & **37.4\(\pm\)**6.9 & **31.4\(\pm\)7.6** & 36.1\(\pm\)8.3 \(\checkmark\) & 34.5\(\pm\)7.8 & **23.3\(\pm\)8.4** & **21.1\(\pm\)9.3** & **22.0\(\pm\)8.3** \\ MW1 & 23.3\(\pm\)5.3 & **17.0\(\pm\)6.8** & 24.8\(\pm\)7.2 \(\checkmark\) & 21.4\(\pm\)7.7 & **19.0\(\pm\)6.4** & **11.9\(\pm\)5.7** & **26.0\(\pm\)8.7** \\ PC1 & 36.3\(\pm\)4.8 & **27.8\(\pm\)3.9** & 38.3\(\pm\)4.4 \(\checkmark\) & 38.6\(\pm\)5.6 & **28.6\(\pm\)5.2** & **26.4\(\pm\)3.6** & **26.1\(\pm\)6.8** \\ PC2 & 13.7\(\pm\)6.0 \(\checkmark\) & **14.4\(\pm\)**8.2 & **0.6\(\pm\)3.3** & **0.2\(\pm\)2.8** & 11.8\(\pm\)7.1 \(\checkmark\) & 13.2\(\pm\)3.3 \(\checkmark\) & **0.1\(\pm\)2.0** \\ PC3 & **28.9\(\pm\)**3.3 & **24.3\(\pm\)3.2** & **23.2\(\pm\)3.7** & **22.4\(\pm\)3.6** & **23.6\(\pm\)3.6** & 28.3\(\pm\)3.4 \(\checkmark\) & **20.1\(\pm\)4.2** \\ PC4 & **55.9\(\pm\)**2.2 & **45.9\(\pm\)2.6** & **53.8\(\pm\)2.9** & **52.9\(\pm\)2.7** & **49.4\(\pm\)2.3** & **49.1\(\pm\)2.1** & **46.6\(\pm\)2.9** \\ JM1 & **22.5\(\pm\)**1.0 & **18.7\(\pm\)1.2** & **21.1\(\pm\)1.1** & **21.0\(\pm\)0.9** & **19.3\(\pm\)1.2** & **19.7\(\pm\)0.8** & **18.0\(\pm\)1.1** \\ \hline
**Average** & 28.5 & 23.6 & 27.5 & 26.9 & 23 & 21.9 & 21.1 \\ \hline
**Improvement of Average** & 20.8\(\%\) & 3.7\(\%\) & 5.8\(\%\) & 23.9\(\%\) & 30.2\(\%\) & 35.1\(\%\) \\ \hline
**Win/Tie/Lose** & 8/3/0 & 6/3/2 & 7/2/2 & 8/3/0 & 8/3/0 & 9/1/1 \\ \hline \hline \end{tabular} Note: (1) all of the numbers are in percentage (\(\%\)) except the last two rows. (2) \(\checkmark\) denotes the corresponding method has no significant difference with the best method. (3) \(\checkmark\), and indicate our CIB method significantly outperforms the corresponding baseline with large, moderate, and small effect size, respectively.  
\end{table} 
Table 6: The comparison results of MCC for all methods in the form of mean \(\pm\) standard deviation. The best value is in boldface. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Our CIB**} & \multicolumn{2}{c}{**MAHAKIL**} & \multirow{2}{*}{**AdaC2 [45]**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{2}{c}{**SMOTE**} & \multirow{2}{*}{**RUS [35]**} & \multicolumn{1}{l}{**None**} \\  & & [44] & & [46] & & [40] & & **RUS [35]** & **None** \\ \hline CM1 & 72.7\(\pm\)2.2 & **59.6\(\pm\)4.4** & 73.4\(\pm\)3.0 \(\checkmark\) & **74.3\(\pm\)3.4** & **61.3\(\pm\)5.2** & **62.9\(\pm\)4.1** & **55.0\(\pm\)5.3** \\ KC1 & 67.8\(\pm\)1.2 \(\checkmark\) & **59.2\(\pm\)