## 4 Experimental Evaluation
### Results  
Table 4 summarizes the recall and precision of our technique on the BigCloneBench dataset. Recall refers to ratio of clone pairs or clone-types within a dataset that our technique detect or classify, while precision is the ratio of clone pairs or their types reported by our technique that are true clone pairs or true clone-types (and not false positives). Overall, our technique achieved 95% precision in the first pass for clone detection. More specifically, our technique achieved 94% recall and 96% precision for true clones, and 97% recall and 95% precision for false clones. In the second pass for clone type classification, our technique achieved 78% recall and precision on average. Our technique achieved high precision (94%) for T1 clones and low precision (67%) for MT3 clones, while the highest recall is for T2 clones (93%) and the lowest is for MT3 clones (58%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Clone Types**} & \multicolumn{4}{c}{**True Clone Pairs**} & \multicolumn{1}{c}{**False Clone**} \\ \hline
**Dataset** & **T1** & **T2** & **ST3** & **MT3** & **WT3/4** & **Pairs** \\ \hline Training & 1099 & 1099 & 1099 & 1098 & 1099 & 5492 \\ Test & 2564 & 2564 & 2564 & 2563 & 2564 & 12,823 \\ Total & 3663 & 3663 & 3663 & 3661 & 3663 & 18,315 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training and test dataset used. \begin{table}
\begin{tabular}{c} Epoch: 200, \\ Batch size: 5 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter settings for each classification pass. In our second experiment, we classified clone-types with and without the first pass. We built a model with the same architecture of our technique, excluding separated passes. Table 5 shows our comparative experimental results. Overall, the proposed two-pass technique achieved better recall and precision than the model without the first pass. With the two-pass technique, classification of T2 clones is significantly improved (see F1 score of Table 5). Table 6 compares the recall and precision of our technique against other approaches. Overall, our technique achieved 96% recall and precision for clone detection, a result significantly better than those of the other approaches, except DeepSim, against which our technique performed slightly lower worse. These results demonstrate that our technique can detect clones at a similar level and rate as DeepSim, despite its much simpler preprocessing. In the case of clone classification, our technique achieved 78% recall and precision. In our final test, we investigated misclassified clones (Table 7). Many WT3/4 clones were incorrectly classified as false clones. The reason for this result was that the similarity ratio of WT3/4 clones was less than 50%. Clones tend to be incorrectly classified as adjacent types, i.e., ST3 clones are misclassified into T2 or MT3 clones. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tools**} & \multicolumn{3}{c}{**Clone Detection**} & \multicolumn{3}{c}{**Clone Type Classification**} \\ \cline{2-7}  & **Recall** & **Precision** & **F1 Score** & **Recall** & **Precision** & **F1 Score** \\ \hline DECKARD [16] & 0.02 & 0.93 & 0.3 & - & - & - \\ RtvNN [23] & 0.01 & 0.95 & 0.01 & - & - & - \\ CDLH [24] & 0.74 & 0.92 & 0.82 & - & - & - \\ DeepSim [20] & 0.98 & 0.97 & 0.98 & - & - & - \\ Our Technique & 0.96 & 0.96 & 0.96 & 0.78 & 0.78 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of existing tools and our technique.