# 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} \hline \hline
**Data** & **Our CIB** & \
\begin{tabular}{c} **MAHAKIL** \\ [44] \\ \end{tabular} & **AdaC2**[45] &
\begin{tabular}{c} **AdaBoost** \\ [46] \\ \end{tabular} & **SMOTE** & **RUS**[35] & **None** \\ \hline CM1 & [16.4, 20.4] & [14.8, 19.0] & [10.2, 15.0] & [11.4, 16.0] & [17.4, 21.4] & [15.3, 19.3] & [9.9, 15.5] \\ KC1 & [24.5, 26.5] & [19.6, 21.6] & [24.9, 26.3] & [23.8, 25.6] & [19.9, 21.9] & [16.7, 18.7] & [20.8, 22.6] \\ KC3 & [26.5, 30.9] & [24.5, 29.5] & [20.1, 26.1] & [20.6, 25.4] & [24.8, 28.6] & [20.3, 26.1] & [22.4, 28.4] \\ MC1 & [21.2, 24.6] & [14.0, 17.6] & [41.4, 45.6] & [41.5, 46.5] & [9.6, 12.4] & [11.9, 13.7] & [10.6, 16.2] \\ MC2 & [34.9, 39.9] & [28.7, 34.1] & [33.1, 39.1] & [31.7, 37.3] & [20.3, 26.3] & [17.8, 24.4] & [19.0, 25.0] \\ MW1 & [21.4, 25.2] & [14.7, 19.5] & [22.2, 27.4] & [18.6, 24.2] & [16.7, 21.3] & [9.9, 13.9] & [22.9, 29.1] \\ PC1 & [34.6, 38.0] & [25.2, 28.8] & [36.7, 39.9] & [36.6, 40.6] & [26.7, 30.5] & [25.1, 27.7] & [23.7, 28.5] \\ PC2 & [11.6, 15.8] & [11.5, 17.3] & [\(-\)0.6, 1.8] & [\(-\)0.8, 1.2] & [9.3, 14.3] & [12.0, 14.4] & [\(-\)0.6, 0.8] \\ PC3 & [27.7, 30.1] & [21.5, 24.5] & [21.9, 24.5] & [21.1, 23.7] & [22.3, 24.9] & [27.1, 29.5] & [18.6, 21.6] \\ PC4 & [55.1, 56.7] & [44.8, 47.4] & [52.3, 54.3] & [51.9, 53.9] & [48.6, 50.2] & [48.3, 49.9] & [45.6, 47.6] \\ JM1 & [22.1, 22.9] & [17.9, 19.1] & [20.6, 21.6] & [20.8, 21.6] & [18.9, 20.1] & [19.2, 20.0] & [17.2, 18.4] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: 95% confidence interval of _MCC_ for CIB and the baselines on 11 NASA datasets. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Data** & **Our CIB** & \ \begin{tabular}{c} **MAHAKIL** \\ [44] \\ \end{tabular}  & **AdaC2**[45] & \begin{tabular}{c} **AdaBoost** \\ [46] \\ \end{tabular}  & **SMOTE** & **RUS**[35] & **None** \\ \hline CM1 & [26.5, 30.1] & [26.8, 30.4] & [16.7, 21.1] & [17.4, 21.4] & [29.1, 32.5] & [28.5, 31.1] & [17.7, 23.1] \\ KC1 & [43.5, 45.1] & [41.0, 42.6] & [42.1, 43.1] & [41.1, 42.3] & [39.6, 41.6] & [42.5, 44.1] & [34.6, 36.6] \\ KC3 & [38.7, 42.1] & [38.0, 42.0] & [30.1, 35.1] & [30.4, 34.6] & [38.7, 41.7] & [37.6, 41.6] & [31.8, 37.0] \\ MC1 & [22.0, 25.6] & [14.9, 18.3] & [39.1, 43.5] & [38.9, 43.9] & [11.7, 14.5] & [9.2, 10.0] & [9.6, 14.2] \\ MC2 & [56.6, 59.8] & [52.6, 55.8] & [53.9, 57.7] & [52.2, 56.4] & [48.2, 52.6] & [49.1, 53.3] & [44.6, 48.6] \\ MW1 & [29.4, 32.6] & [24.3, 28.5] & [27.7, 32.3] & [23.7, 28.7] & [26.4, 30.4] & [22.4, 25.0] & [26.5, 32.3] \\ PC1 & [39.9, 43.1] & [31.8, 35.0] & [39.3, 42.1] & [38.5, 42.3] & [33.4, 36.6] & [29.3, 30.9] & [28.3, 33.1] \\ PC2 & [12.8, 16.8] & [12.9, 18.5] & [0.3, 2.5] & [\(-\)0.1, 1.7] & [11.0, 15.6] & [9.0, 10.0] & [\(-\)0.1, 1.3] \\ PC3 & [36.7, 38.7] & [31.4, 34.2] & [27.7, 30.1] & [26.9, 29.1] & [32.6, 34.8] & [35.9, 37.5] & [25.3, 28.7] \\ PC4 & [61.4, 62.8] & [52.7, 54.9] & [57.7, 59.5] & [57.3, 59.1] & [55.9, 57.3] & [54.0, 55.0] & [52.0, 54.0] \\ JM1 & [37.0, 37.8] & [33.7, 34.9] & [33.5, 34.3] & [33.7, 34.3] & [33.3  
**Conclusion:** on average, CIB always obtains the best performance in terms of \(F_{1}\), AGF, MCC, and AUC and improves \(F_{1}\)/AGF/MCC/AUC over the baselines by at least 9%/0.2%/3.7%/1.2%. In most cases, our CIB significantly outperforms the baselines with at least medium effect size in terms of \(F_{1}\), MCC, and AUC.