# A Multi-Factor Approach for Selection of Developers to Fix Bugs in a Program
## 4 Experiments
### Experiments Results
#### 4.2.2 Performance Comparison between Our Proposed ABC-DR Method and B-Based and D-Based Components  
Our proposed ABC-DR method consists of two parts (B-based component analysis and D-based component analysis), and we examined the performance of each component. We needed to see if the combined performance of the two components has improved when compared to the performances of the individual components. The experiments were done for the GCC, Mozilla, and NetBeans datasets, and recall@5, precision@5, recall@10, and precision@10 were used as evaluation criteria. In Table 5, it can be noted that the ABC-DR method has better performance when compared to the individual components for the GCC, Mozilla, and NetBeans datasets. For the GCC dataset, the results of ABC-DR method are 41.30%, 24.90%, 51.10%, and 15.40%. It can be seen in Table 5 that the ABC-DR method shows the best performance and the B-based component approach performs better than the \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Project** & \multicolumn{3}{c}{**ABC-DR**} & **DevRec** & **Imp.De** & **DREX** & **Imp.DR** & **Buggie** & **Imp.Bu** \\ \hline \multirow{3}{*}{GCC} & precision@5(\%) & 24.9 & 24 & 3.75 & 24.4 & 2.04 & 22.6 & 10.17 \\ \cline{2-9}  & precision@10(\%) & 15.4 & 15 & 2.66 & 15.7 & \(-\)1.91 & 17.3 & \(-\)10.98 \\ \hline \multirow{3}{*}{Mozilla} & precision@5(\%) & 23.3 & 21.9 & 6.39 & 13.6 & 71.32 & 15.3 & 52.28 \\ \cline{2-9}  & precision@10(\%) & 11.9 & 10.4 & 14.42 & 9.84 & 20.93 & 12.7 & -6.29 \\ \hline \multirow{3}{*}{NetBeans} & precision@5(\%) & 27.9 & 27 & 3.33 & 23.2 & 20.25 & 15.8 & 76.58 \\ \cline{2-9}  & precision@10(\%) & 14.9 & 14.4 & 3.47 & 14.4 & 3.47 & 13.2 & 12.87 \\ \hline \multirow{3}{*}{Aver} & precision@5(\%) & 25.55 & 24.275 & 5.33 & 20.85 & 27.7925 & 16.25 & 67.5 \\ \cline{2-9}  & precision@10(\%) & 14.3 & 13.225 & 8.7625 & 13.635 & 6.305 & 13.175 & 13.3725 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: precision@5 and precision@10 of our proposed ABC-DR and DevRec, DREX, and Bugzie. It also shows the improvement of ABC-DR over DevRec (Imp.De), the improvement of ABC-DR over DBEX (Imp.DR), and the improvement of ABC-DR over Bugzie (Imp.Bu). \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**project** & \multicolumn{3}{c}{**ABC-DR**} & **DevRec** & **Imp.De** & **DREX** & **Imp.DR** & **Buggie** & **Imp.Bu** \\ \hline \multirow{3}{*}{GCC} & recall@5(\%) & 41.3 & 39.8 & 3.76 & 41.1 & 0.72 & 38.1 & 8.39 \\ \cline{2-9}  & recall@10(\%) & 51.1 & 49.8 & 2.61 & 51.7 & \(-\)1.16 & 57.6 & \(-\)11.28 \\ \hline \multirow{3}{*}{Mozilla} & recall@5(\%) & 39.6 & 34.7 & 14.12 & 23.8 & 66.38 & 23.4 & 69.23 \\ \cline{2-9}  & recall@10(\%) & 40.3 & 38.1 & 5.77 & 32.3 & 24.76 & 38.4 & 4.94 \\ \hline \multirow{3}{*}{NetBeans} & recall@5(\%) & 51.2 & 49.3 & 3.85 & 41.9 & 22.19 & 26.3 & 94.67 \\ \cline{2-9}  & recall@10(\%) & 53.6 & 51.6 & 3.87 & 51 & 5.09 & 44.2 & 21.26 \\ \hline \multirow{3}{*}{Aver} & recall@5(\%) & 43.625 & 40.725 & 9.425 & 25.385 & 26.2825 & 26.125 & 81.545 \\ \cline{2-9}  & recall@5(\%) & 48.35 & 45.4 & 6.8025 & 45.4 & 8.1375 & 42.225 & 20.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: recall@5 and recall@10 of our proposed ABC-DR and DevRec, DREX, and Bugzie. It also shows the improvement of ABC-DR over DevRec (Imp.De), the improvement of ABC-DR over DREX (Imp.DR), and the improvement of ABC-DR over Bugzie (Imp.Bu). D-based component approach. For the Mozilla dataset, the results of ABC-DR method are 39.60%, 23.30%, 40.30%, and 11.90%, which have significantly improved compared to the B-based component method and the D-based component method. The ABC-DR method performs best compared to the B-based component and the D-based component methods. For the NetBeans dataset, the results of ABC-DR method are 51.20%, 27.90%, 53.60%, and 14.90%; the results of B-based component method are 37.10%, 20.70%, 45.90%, and 12.90%; and the results of D-based component method are 33.10%, 18.70%, 45.30%, and 12.80%, respectively.