## 4 Discussion  
This section provides a comprehensive comparative analysis of our results in the context of related work in the literature. The intention is to juxtapose our results with similar research works [16; 17; 18], considering various aspects such as the nature of the problem, the quality of the dataset, and the main results obtained, with the aim of highlighting the significance of our contributions and identifying areas of improvement.  
Table 5 provides an overview of the deep learning models used to classify UML diagrams, along with the results performance in terms of test accuracy and F1-score.  
It is noteworthy that the best-performing model in this research, ViT_h_14, secures the second position in terms of the highest accuracy, being only surpassed by [16]. However, as indicated in Table 5, their approach encompassed just four UML classes and an additional non-UML class, simplifying the problem. In addition, another very important factor to take into account when analyzing the obtained results is the amount of repeated images in the datasets, which can result in performance metrics appearing more favorable.  
Moreover, ViT_h_14 significantly outperforms its competitors in terms of F1-score. The rationale behind this fact could be that [16; 17] incorporated non-UML images into their studies, causing the models not to focus as intensely on UML diagram features, consequently producing a notable decrease in their F1-values. This achievement in the F1 metric highlights the robust performance of this research model across all classes, despite facing specific challenges, notably in the case of component diagrams, as depicted in Figure 2.  
Therefore, after carefully analyzing all the results obtained, both in our work and in previous studies, we can conclude that our results are more than satisfactory. Not only have we achieved similar or superior accuracy, but we have done so despite the significant  
Figure 2: Radar chart of the F1-score per class (STTL).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Research**} & **Model** & **Number of** & **Duplicates** & **Test** & **Test** \\  & **Architecture** & **Classes** & **(\%)** & **Accuracy** & **F1-Score** \\ \hline [PERSON] et al. & & 1 & & & \\ (I) [16] & MobileNet & 4 + 1 & 9\% & 96.8\% & 91.91\% \\ [PERSON] et al. & & 1 & & & \\ (II) [17] & DenseNet-169 & 10 + 1 & 7\% & 87.22\% & 83.44\% \\ [PERSON] et al. [18] & Inception\_V3 & 6 & 12\% & 87.8\% & NA \\ This research & ViT\_h\_14 & 6 & 0\% & 92.7\% & 92.4\% \\ \hline \hline \end{tabular}  
* Observe that +1 denotes an additional class for non-UML images.  
\end{table}
Table 5: Results analysis and discussion with previous related research in STTL.  
difference in the percentage of duplicate elements in datasets from the literature (ranging from 7% to 12% in other datasets, while ours has 0% duplicate elements). Additionally, the F1-score we have obtained surpasses all those previously reported in other studies. It is also worth noting that our work demonstrates the suitability of ViTs for UML diagram classification, setting our approach apart from others that predominantly used CNNs for similar tasks.