# Automatic Bug Triaging via Deep Reinforcement Learning
## 5 Results
### Experimental Results
As shown in Tables 3-5, the BT-RL method showed more significant improvements in the accuracy of Top1-Top5 compared with the LDA_KL, LDA_SVM, DERTOM, and DeepTriaging methods on the four datasets. We conducted experiments on bug triaging in different time periods. Compared with other benchmark methods, BT-RL yielded better experimental results, proving its reliability. First, because all baselines were trained based on topic models, they were used to identify the hidden topic information in the document set or corpus. By adopting the bag-of-words method, the documents were converted into word frequency vectors, but the relationship between words was not considered. We used Word2 vec for text vectorization, which not only solved the problem of dimensionality disaster, but also mined the associated attributes between words, thereby improving the semantic representation of vectors. Second, regarding vector fusion, other benchmark methods used stitching; however, we used multiplication between elements for a more accurate information representation, which will be advantageous in the follow-up processes. Third, as the baselines were based on partial data training and partial data verification, and when the two data parts did not overlap, the effect was poor. However, for a new task, BT-RL was learning as it was being trained, and this mechanism enabled BT-RL to fully learn the information in the data. Equation (2) Validation considering effects of multiple sources of information. We regarded different factors as the input of the model, such as only the text information of the bug report or the activity information of the developer, or both. We wished to investigate whether the factors above affected the accuracy of the BT-RL method in the bug dispatch of open-source data (NetBeans, OpenOffice, Mozilla, and Eclipse) projects. Hence, we designed three groups of comparative experiments: (1) only consider text information (BT-RL-T), (2) only consider developer activity information (BT-RL-D), and (3) consider both (1) and (2) (BT-RL-TD). Table 6 shows the effect of considering different data as inputs on the results of BT-RL. \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{4}{*}{NetBeans} & Top5 & 0.50 & 0.52 & 0.48 & 0.31 & 0.54 \\  & Top4 & 0.46 & 0.46 & 0.44 & 0.31 & 0.48 \\  & Top3 & 0.40 & 0.44 & 0.40 & 0.27 & 0.48 \\  & Top2 & 0.36 & 0.40 & 0.38 & 0.25 & 0.46 \\  & Top1 & 0.20 & 0.34 & 0.34 & 0.23 & 0.38 \\ \hline \multirow{4}{*}{Mozilla} & Top5 & 0.46 & 0.48 & 0.30 & 0.10 & 0.50 \\  & Top4 & 0.36 & 0.38 & 0.24 & 0.10 & 0.44 \\  & Top3 & 0.32 & 0.36 & 0.20 & 0.10 & 0.36 \\  & Top2 & 0.28 & 0.22 & 0.16 & 0.06 & 0.30 \\  & Top1 & 0.14 & 0.10 & 0.04 & 0.04 & 0.22 \\ \hline \multirow{4}{*}{Eclipse} & Top5 & 0.50 & 0.50 & 0.48 & 0.40 & 0.58 \\  & Top4 & 0.40 & 0.50 & 0.42 & 0.35 & 0.54 \\  & Top3 & 0.32 & 0.40 & 0.38 & 0.25 & 0.44 \\  & Top2 & 0.22 & 0.36 & 0.34 & 0.23 & 0.36 \\  & Top1 & 0.12 & 0.32 & 0.28 & 0.21 & 0.22 \\ \hline \multirow{4}{*}{OpenOffice} & Top5 & 0.34 & 0.46 & 0.38 & 0.27 & 0.50 \\  & Top4 & 0.32 & 0.40 & 0.36 & 0.23 & 0.40 \\ \cline{1-1}  & Top3 & 0.28 & 0.34 & 0.30 & 0.23 & 0.40 \\ \cline{1-1}  & Top2 & 0.22 & 0.30 & 0.22 & 0.02 & 0.34 \\ \cline{1-1}  & Top1 & 0.06 & 0.22 & 0.20 & 0.02 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The accuracy of BT-RL and other benchmarks on four projects of the third experimental window. Table 6 shows that BT-RL-TD yielded significant improvement in terms of the accuracy of Top1-Top5 compared with BT-RL-T and BT-RL-D on the four datasets. The accuracy of Top5 of BT-RL-TD increased by 35.00% compared with that of BT-RL-T, whereas it was the same as that of BT-RL-D for the NetBeans dataset. The accuracy of Top5 of BT-RL-TD increased by 30.76% and 6.25% compared with those of BT-RL-T and BT-RL-D, respectively, for the Mozilla dataset.