# Automatic Bug Triaging via Deep Reinforcement Learning
## 5 Results
### Experimental Results  
In this section, we discuss and analyze the experimental results based on specific experimental problems. Equation (1) Compared with the baseline (unsupervised method), does the proposed BT-RL model improve the accuracy of bug triaging? We selected some unsupervised methods, including LDA_KL, LDA_SVM, DERTOM, and DeepTriaging, for bug triaging. We aimed to prove whether BT-RL can achieve a better allocation effect on open-source datasets than the baseline method, and the accuracy rate of Top1-Top5 was used as the evaluation standard. We partitioned the entire dataset into train:test window sizes of 1000:50 for the experiments. Tables 3-5 show the accuracy of BT-RL and other methods in bug triaging. Our comparative experiments refer to the data of the original paper, and the reference list provides the references. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Project** & **Top-k** & **LDA\_KL** & **LDA\_SVM** & **DERTOM** & **DeepTriaging** & **BT-RL** \\ \hline \multirow{6}{*}{NetBeans} & Top5 & 0.48 & 0.70 & 0.62 & 0.44 & 0.76 \\  & Top4 & 0.36 & 0.60 & 0.60 & 0.35 & 0.72 \\ \cline{1-1}  & Top3 & 0.28 & 0.58 & 0.54 & 0.35 & 0.64 \\ \cline{1-1}  & Top2 & 0.20 & 0.48 & 0.44 & 0.31 & 0.54 \\ \cline{1-1}  & Top1 & 0.10 & 0.36 & 0.28 & 0.21 & 0.40 \\ \hline \multirow{6}{*}{Mozilla} & Top5 & 0.36 & 0.52 & 0.52 & 0.38 & 0.52 \\ \cline{1-1}  & Top4 & 0.32 & 0.48 & 0.46 & 0.33 & 0.50 \\ \cline{1-1}  & Top3 & 0.26 & 0.38 & 0.42 & 0.33 & 0.44 \\ \cline{1-1}  & Top2 & 0.18 & 0.32 & 0.34 & 0.29 & 0.32 \\ \cline{1-1}  & Top1 & 0.06 & 0.12 & 0.18 & 0.13 & 0.22 \\ \hline \multirow{6}{*}{Eclipse} & Top5 & 0.42 & 0.52 & 0.44 & 0.40 & 0.64 \\ \cline{1-1}  & Top4 & 0.38 & 0.46 & 0.42 & 0.23 & 0.62 \\ \cline{1-1}  & Top3 & 0.24 & 0.44 & 0.42 & 0.19 & 0.56 \\ \cline{1-1}  & Top2 & 0.18 & 0.28 & 0.36 & 0.13 & 0.42 \\ \cline{1-1}  & Top1 & 0.08 & 0.22 & 0.26 & 0.08 & 0.26 \\ \hline \multirow{6}{*}{OpenOffice} & Top5 & 0.40 & 0.46 & 0.42 & 0.15 & 0.48 \\ \cline{1-1}  & Top4 & 0.40 & 0.38 & 0.40 & 0.08 & 0.40 \\ \cline{1-1}  & Top3 & 0.32 & 0.32 & 0.32 & 0.04 & 0.38 \\ \cline{1-1}  & Top2 & 0.22 & 0.28 & 0.30 & 0.04 & 0.28 \\ \cline{1-1}  & Top1 & 0.16 & 0.14 & 0.16 & 0.04 & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy of BT-RL and other benchmarks on four projects of the second experimental window. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Project** & **Top-k** & **LDA\_KL** & **LDA\_SVM** & **DERTOM** & **DeepTriaging** & **BT-RL** \\ \hline \multirow{6}{*}{NetBeans} & Top5 & 0.28 & 0.48 & 0.48 & 0.33 & 0.54 \\  & Top4 & 0.26 & 0.48 & 0.44 & 0.33 & 0.52 \\ \cline{1-1}  & Top3 & 0.20 & 0.46 & 0.36 & 0.33 & 0.48 \\ \cline{1-1}  & Top2 & 0.18 & 0.40 & 0.32 & 0.18 & 0.44 \\ \cline{1-1}  & Top1 & 0.10 & 0.26 & 0.10 & 0.04 & 0.34 \\ \hline \multirow{6}{*}{Mozilla} & Top5 & 0.46 & 0.62 & 0.54 & 0.52 & 0.68 \\ \cline{1-1}  & Top4 & 0.42 & 0.60 & 0.52 & 0.52 & 0.66 \\ \cline{1-1}  & Top3 & 0.36 & 0.60 & 0.46 & 0.48 & 0.64 \\ \cline{1-1}  & Top2 & 0.32 & 0.50 & 0.40 & 0.38 & 0.52 \\ \cline{1-1}  & Top1 & 0.16 & 0.20 & 0.16 & 0.20 & 0.28 \\ \hline \multirow{6}{*}{Eclipse} & Top5 & 0.58 & 0.74 & 0.72 & 0.47 & 0.78 \\ \cline{1-1}  & Top4 & 0.50 & 0.70 & 0.62 & 0.37 & 0.72 \\ \cline{1-1}  & Top3 & 0.42 & 0.66 & 0.58 & 0.27 & 0.68 \\ \cline{1-1}  & Top2 & 0.36 & 0.52 & 0.52 & 0.18 & 0.58 \\ \cline{1-1}  & Top1 & 0.16 & 0.40 & 0.32 & 0.12 & 0.46 \\ \hline \multirow{6}{*}{OpenOffice} & Top5 & 0.44 & 0.50 & 0.46 & 0.20 & 0.52 \\ \cline{1-1}  & Top4 & 0.40 & 0.44 & 0.38 & 0.20 & 0.46 \\ \cline{1-1}  & Top3 & 0.32 & 0.42 & 0.36 & 0.20 & 0.42 \\ \cline{1-1}  & Top2 & 0.22 & 0.30 & 0.28 & 0.16 & 0.30 \\ \cline{1-1}  & Top1 & 0.16 & 0.26 & 0.20 & 0.10 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy of BT-RL and other benchmarks on four projects of the first experimental window.