# Automatic Bug Triaging via Deep Reinforcement Learning
## 5 Results
### Experimental Results
The accuracy of Top5 of BT-RL-TD increased by 85.71% and 56.00% compared with those of BT-RL-T and BT-RL-D, respectively, for the Eclipse dataset. The accuracy of Top5 of BT-RL-TD increased by 44.44% and 8.33% compared with those of BT-RL-T and BT-RL-D, respectively, for the OpenOffice dataset. Therefore, it was effective to consider the multiple information sources of bug reports. Equation (3) Verify effects of different feature fusion methods. We wish to understand whether the use of different fusion methods for text and developer engagement features to assign bug reports in BT-RL will affect the accuracy. Hence, we designed three experiments to verify the effects of different feature fusion methods on BT-RL: (1) use BT-RL-Con, (2) use BT-RL-Add, and (3) use BT-RL-Mul. The premise of adding and multiplying elements is to ensure that the two fused vector dimensions are the same. If the dimensions are different, they can be transformed into the same dimension vector through linear transformation. Table 7 shows the effects of different data fusion approaches on the BT-RL results. Table 7 shows that BT-RL-Mul yielded significant improvement in terms of the accuracy of Top1-Top5 compared with BT-RL-Add and BT-RL-Con for the four datasets. The accuracy of Top5 of BT-RL-Mul increased by 8.00% and 17.39% compared with those of BT-RL-Add and BT-RL-Con, respectively, for the NetBeans dataset. The accuracy of Top5 of BT-RL-Mul increased by 21.42% and 36.00% compared with those of BT-RL-Add and BT-RL-Con, respectively, for the Mozilla dataset. The accuracy of Top5 of BT-RL-Mul increased by 30.00% and 39.28% compared with those of BT-RL-Add and BT-RL-Con, respectively, for the Eclipse dataset. The accuracy of Top5 of BT-RL-Mul increased by 13.04% and 18.18% compared with those of BT-RL-Add and BT-RL-Con, respectively, for the OpenOffice dataset. Addition and multiplication between elements increased the amount of information in each dimension, which was performed intentionally for the final distribution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Top-k** & **BT-RL-T** & **BT-RL-D** & **BT-RL-TD** \\ \hline \multirow{6}{*}{NetBeans} & Top5 & 0.40 & 0.54 & 0.54 \\  & Top4 & 0.32 & 0.50 & 0.52 \\  & Top3 & 0.32 & 0.46 & 0.48 \\  & Top2 & 0.28 & 0.44 & 0.44 \\  & Top1 & 0.26 & 0.32 & 0.34 \\ \hline \multirow{6}{*}{Mozilla} & Top5 & 0.52 & 0.64 & 0.68 \\  & Top4 & 0.50 & 0.64 & 0.66 \\ \cline{1-1}  & Top3 & 0.48 & 0.62 & 0.64 \\ \cline{1-1}  & Top2 & 0.44 & 0.52 & 0.52 \\ \cline{1-1}  & Top1 & 0.20 & 0.38 & 0.28 \\ \hline \multirow{6}{*}{Eclipse} & Top5 & 0.42 & 0.50 & 0.78 \\  & Top4 & 0.38 & 0.50 & 0.72 \\ \cline{1-1}  & Top3 & 0.32 & 0.50 & 0.68 \\ \cline{1-1}  & Top2 & 0.26 & 0.46 & 0.58 \\ \cline{1-1}  & Top1 & 0.22 & 0.26 & 0.46 \\ \hline \multirow{6}{*}{OpenOffice} & Top5 & 0.36 & 0.48 & 0.52 \\ \cline{1-1}  & Top4 & 0.34 & 0.42 & 0.46 \\ \cline{1-1}  & Top3 & 0.28 & 0.38 & 0.42 \\ \cline{1-1}  & Top2 & 0.22 & 0.24 & 0.30 \\ \cline{1-1}  & Top1 & 0.14 & 0.20 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The accuracy of BT-RL considering different information. The experimental results of different windows obtained in the NetBeans, Mozilla, Eclipse, and OpenOffice datasets are shown in Tables 3-5, respectively. The research questions posed herein were answered based on the obtained results. In most cases, the accuracy of the Top5 developers' sequence recommended by our method was higher than that of the baseline.