# A Multi-Factor Approach for Selection of Developers to Fix Bugs in a Program
## 4 Experiments
### Experiments Results
#### 4.2.3 The Effect of Fifteen Weight Settings on the Performance of the ABC-DR Method  
To answer this question, we randomly selected fifteen groups of parameter values and used them to determine the contribution of each part to the performance of the ABC method, i.e., fifteen groups of \(\gamma_{1},\gamma_{2},\gamma_{3},\gamma_{4},\gamma_{5}\). Comparing them with the parameters determined by our proposed ABC-DR algorithm, we needed to explore the effects of the different parameter values on the experimental results and whether the parameters measured by artificial bee colony algorithm are optimal. We performed the experiment for the GCC, Mozilla, and NetBeans datasets, and used the recall@5, precision@5, recall@10, and precision@10 as the evaluation standard. Tables 6-8 show our experimental results for three datasets. the parameters measured by the ABC-DR algorithm have better performance on fixing bug reports. In Table 8, it can be seen that the parameters measured by our proposed ABC-DR algorithm have the highest recall@5 and precision@5 for the NetBeans dataset, and the highest recall@10 and precision@10 for parameters 0.88, 0.19, 0.87, 0.62, and 0.09, which are 0.539 and 0.153, respectively. Parameters 0.17, 0.333, 0.03, 0.69, and 0.87 have the lowest recall@5 and precision@5, 0.282 and 0.161, respectively, and have the worst performance in repairing bug reports. On the whole, the parameters we measured have better performance on fixing bug reports. In short, the parameters calculated by artificial bee colony algorithm are the optimal parameters, and our ABC-DR algorithm has the best performance in fixing bug reports. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Project** & \multicolumn{2}{c}{**GCC**} & & & & & & \\ \hline \multicolumn{8}{c}{**Five-Part**} & \multicolumn{2}{c}{**Parameter Setting**} & & & \\ \hline
**ID** & \(\gamma_{1}\) & \(\gamma_{2}\) & \(\gamma_{3}\) & \(\gamma_{4}\) & \(\gamma_{5}\) & **recall@5** & **precision@5** & **recall@10** & **precision@10** \\ \hline
1 & 0.48 & 0.01 & 0.93 & 0.68 & 0.94 & 0.195 & 0.122 & 0.361 & 0.111 \\ \hline
2 & 0.91 & 0.3 & 0.29 & 0.05 & 0.61 & 0.287 & 0.179 & 0.436 & 0.134 \\ \hline
3 & 0.94 & 0.12 & 0.48 & 0.16 & 0.88 & 0.224 & 0.144 & 0.392 & 0.121 \\ \hline
4 & 0.87 & 0.66 & 0.23 & 0.52 & 0.64 & 0.339 & 0.207 & 0.47 & 0.144 \\ \hline
5 & 0.25 & 0.38 & 0.17 & 0.81 & 0.54 & 0.273 & 0.171 & 0.462 & 0.141 \\ \hline
6 & 0.81 & 0.71 & 0.64 & 0.35 & 0.33 & 0.374 & 0.226 & 0.502 & 0.154 \\ \hline
7 & 0.51 & 0.16 & 0.99 & 0.44 & 0.36 & 0.312 & 0.193 & 0.458 & 0.14 \\ \hline
8 & 0.68 & 0.34 & 0.54 & 0.54 & 0.44 & 0.337 & 0.206 & 0.469 & 0.144 \\ \hline
9 & 0.77 & 0.24 & 0.47 & 0.86 & 0.74 & 0.269 & 0.169 & 0.439 & 0.134 \\ \hline
10 & 0.55 & 0.36 & 0.01 & 0.06 & 0.01 & 0.392 & 0.236 & 0.507 & 0.156 \\ \hline
11 & 0.93 & 0.14 & 0.18 & 0.29 & 0.22 & 0.363 & 0.221 & 0.474 & 0.146 \\ \hline
12 & 0.12 & 0.25 & 0.95 & 0.15 & 0.92 & 0.157 & 0.117 & 0.361 & 0.112 \\ \hline
13 & 0.11 & 0.18 & 0.49 & 0.54 & 0.26 & 0.284 & 0.176 & 0.467 & 0.143 \\ \hline
14 & 0.36 & 0.09 & 0.85 & 0.38 & 0.73 & 0.194 & 0.127 & 0.381 & 0.118 \\ \hline
15 & 0.29 & 0.46 & 0.81 & 0.96 & 0.69 & 0.264 & 0.165 & 0.457 & 0.139 \\ \hline  & **0.4** & **0.08** & **0.06** & **1** & **0.01** & **0.413** & **0.249** & **0.51** & **0.154** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of different parameter settings on experimental results for the GCC dataset, and the last behavior in the table is the parameters measured by our proposed ABC-DR algorithm for the GCC dataset.