## 7 Evaluation
### Experimental Results  
The results of the experiment, together with the main descriptive statistics of the performed transformations, are presented in Table 2. In this table, the \"Number of executed transformations column\" shows the number of transformations performed during the experiment, while the \"Number of executed atomic transformations\" column presents the actual number of operations performed; the \"Number of expected output elements\" column shows the total number of elements (I\({}_{\text{TMT}}\)) per model acquired after manual extraction, and this is considered as the benchmark result; the \"Number of output elements\" columns for the original and NLP-enhanced solution sections indicate the total number of elements that were acquired after transformation processing of each model. The \"MeanDiff\", \"Accuracy\", and \"Jaccard\" columns show the evaluation results obtained with the measures presented in Section 7.3. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Number of**} & \multicolumn{3}{c}{**Number of**} & \multicolumn{3}{c}{**Number of**} & \multicolumn{3}{c}{**Original Solution [14]**} & \multicolumn{3}{c}{**NLP-Enhanced Solution**} \\ \cline{3-13}
**Model No.** & \begin{tabular}{c} **Executed** \\ **Transformations** \\ \end{tabular} & \begin{tabular}{c} **Executed Atomic** \\ **Transformations** \\ \end{tabular} & \begin{tabular}{c} **Expected Output** \\ **Elements** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Elements** \\ \end{tabular} & \
\begin{tabular}{c} **MeanDiff** \\ **Elements** \\ \end{tabular} & **Accuracy** & **Jaccard** &
\begin{tabular}{c} **Number of** \\ **Elements** \\ \end{tabular} & **MeanDiff** & **Accuracy** & **Jaccard** \\ \hline UCM 5 & 1 & 3 & 9 & 9 & 0 & 1 & 1 & 9 & 0 & 1 & 1 \\ \hline UCM 6 & 1 & 3 & 9 & 9 & 0 & 1 & 1 & 7 & 0.667 & 0.333 & 0.333 \\ \hline UCM 7 & 1 & 4 & 12 & 12 & 0 & 1 & 1 & 11 & 0.25 & 0.75 & 0.75 \\ \hline UCM 8 & 1 & 4 & 12 & 12 & 0 & 1 & 1 & 11 & 0.25 & 0.75 & 0.75 \\ \hline UCM 9 & 1 & 4 & 12 & 12 & 0 & 1 & 1 & 12 & 0 & 1 & 1 \\ \hline UCM 10 & 1 & 3 & 9 & 9 & 0 & 0.667 & 0.667 & 8 & 0.333 & 0.667 & 0.667 \\ \hline UCM 11 & 1 & 2 & 6 & 6 & 0 & 0.5 & 0.667 & 6 & 0 & 0.5 & 0.75 \\ \hline UCM 12 & 1 & 7 & 21 & 21 & 0 & 1 & 1 & 21 & 0 & 1 & 1 \\ \hline UCM 13 & 2 & 3 & 9 & 9 & 0 & 1 & 1 & 8 & 0.333 & 0.667 & 0.667 \\ \hline UCM 14 & 2 & 2 & 6 & 6 & 0 & 1 & 1 & 6 & 0 & 1 & 1 \\ \hline UCM 15 & 1 & 5 & 15 & 0 & 0.6 & 0.6 & 15 & 0 & 1 & 1 \\ \hline UCM 16 & 2 & 3 & 9 & 9 & 0 & 1 & 1 & 9 & 0 & 0.333 & 0.333 \\ \hline UCM 17 & 1 & 2 & 6 & 6 & 0 & 0.5 & 0.5 & 6 & 0 & 1 & 1 \\ \hline UCM 18 & 1 & 1 & 3 & 3 & 0 & 1 & 1 & 3 & 0 & 1 & 1 \\ \hline UCM 19 & 1 & 5 & 14 & 15 & 0.2 & 0.8 & 0.8 & 14 & 0 & 0.6 & 0.7 \\ \hline UCM 20 & 1 & 1 & 3 & 3 & 0 & 1 & 1 & 3 & 0 & 1 & 1 \\ \hline \multicolumn{7}{c}{Mean values:} & & 0.121 & 0.663 & 0.705 & & 0.194 & 0.755 & 0.789 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._The results indicate multiple cases where the NLP-enhanced solution clearly outperformed the original solution. As expected, more in-depth analysis of those cases showed that certain input models contained elements whose names included multiple words in the formulation of noun and verb phrases; additionally, some of those phrases contained articles and other linguistic units that could not be properly handled by the original solution. The NLP enhancements also proved beneficial in processing more complex cases containing model element names with conjunctive/disjunctive clauses. The mean values of transformation accuracy and Jaccard-based measures also confirm the superiority of our new development over the original one. The mean Jaccard distance-based measure between manual expert-based results and results acquired from the NLP-enhanced solution was 0.789, indicating that, on average, almost 80% of the expected output elements were generated successfully per model.