# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 6 Evaluation Methods and Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Work** & **Methods** & **Effects** & **Dataset** \\ \hline Triage Using CNN and RF with Boosting & CNN, and boosting-enhanced random forest & 96.34\% accuracy, and 96.43\% F score & Mozilla, Eclipse, JBoss, OpenFOAM, and Firefox \\ \hline \multirow{2}{*}{\begin{tabular}{l} itriage \\ \end{tabular} } & Tosing sequence model and RNN with GRU & 9-39\% top-one accuracy improvement & 210 from Eclipse, 300 k from Mozilla, and 165 k from Centoo \\ \hline DeepTriage-mani & Bidirectional RNN and softmax classifier & 34–47\% accuracy & 383 k from Chromium, 314 k from Mozilla \\  & & & Core, and 162 k from Mozilla Firefox \\ \hline Triage based on bug cause & Enhanced LDA & 64\% F score & 1k+ from Apache, Eclipse, and Mozilla \\ \hline \multirow{2}{*}{\begin{tabular}{l} Partially supervised neural network for \\ dedupplication and clustering \\ \end{tabular} } & Word embedding, bidirectional GRU units, topic clustering, and conditional & 0.95 and 0.88 F score for Firefox and JDT, respectively & 17 k from SnapSR, 46 k from Eclipse, and 34 k from Firefox \\  & attention-based dedupplication & & 39\% Frecision, and 90.17\% recall rate & 11 k from Ericsson \\ \hline \multirow{2}{*}{\begin{tabular}{l} T-REC \\ classifier \\ \end{tabular} } & TF-IDE, one-hot encoding, and logistic & 89.75\% Precision, and 90.17\% recall rate & 11 k from Ericsson \\  & & & 76.17\% CC08, 83.6\% ACC610, and 89.7\% & 9.5 M from Sida \\ \hline \multirow{2}{*}{\begin{tabular}{l} Developer activity-motivated triage \\ \end{tabular} } & Vector space model, BM25F, and noisy-or & 76.17\% CC0820 & 83.6\% ACC610, and 89.7\% & 9.5 M from Sida \\  & classifier & & & \\ \hline \multirow{2}{*}{\begin{tabular}{l} Developer activity-motivated triage \\ \end{tabular} } & Word2 vec and CNN & 0.7489 top-10 accuracy & 39 k from Eclipse, 15 k from Mozilla, and 19 k from Netbears \\ \hline \multirow{2}{*}{\begin{tabular}{l} AI-based document generation model \\ \end{tabular} } & LDA and backpropagation & Over 84\% accuracy & 3 k from Buggilla and 41 k from MSR \\ \hline Triage for industrial environments & LDA and DNN & 85.1\%, 70.1\%, and 92.1\% RR85 for JDT, Platform, and Firefox, respectively & 1 k from JDT, 4 k from Platform, and 13 k from Firefox \\ \hline NLP-based triage & Word2 vec and LSTM & \(\sim\)78\% accuracy & Not mentioned \\ \hline \multirow{2}{*}{\begin{tabular}{l} Hierarchical attention network for triage \\ network \\ \end{tabular} } & Word2 Vec, GloVe, and hierarchical attention & 50–65\% accuracy & 633 k from Chromium, 1 M from Core, 1 M from Firefox, 187 k from Netbears, and 318 k from Eclipse \\ \hline \multirow{2}{*}{\
\begin{tabular}{l} Mixed DNN for triage \\ \end{tabular} } & LSTM and CNN & 80\% and 60\% top-five accuracy for Eclipse and Mozilla, respectively & 200 k from Eclipse and 202 k from Mozilla \\ \hline \multirow{2}{*}{
\begin{tabular}{l} BTCSR \\ \end{tabular} } & TF-IDE, LDA, random walk, and cooperative & 51.26\% RR83, 63.25\% RR85, and 74.14\% & 14 k from Eclipse, 10 k from Mozilla, 11 k from Nebears, and 2 k from GCC \\  & SkipGram & RR10 & 14 k from Eclipse, 10 k from Platform, and 37 k from \\ \hline Triage using GCN & TF-IDE and graph convolutional network & 84\%, 72.11\%, and 66.5\% top-10 accuracy for & 1 k from JDT, 4 k from Platform, and 37 k from Firefox \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._