## 5 Experimental Evaluation
'n/a' indicates that particular rules could not be invoked on these cases due to the absence of UML elements in the model required for execution. We excluded rules T\({}_{6}\) and T\({}_{20}\) from evaluation due to certain NLP processing requirements, which are out of scope in this paper. While the nature of the predefined transformations suggests that these values should be equal to 1.0, this is not always true for the transformations that apply two or more text processing operators on the model elements possessing ill-named labels. Table 2 shows that transformations comprising mappings with EXTRACTNOUN/EXTRACTVERB operators often failed to achieve maximum accuracy values. This is explained by the fact that to apply both these operations successfully, text labels must contain at least two words, which was not always the case. There were cases when a UseCase element name contained a single word (e.g., '_Login_' and '_Start_'), which resulted in the extraction of valid verbs, but also in failure to extract noun phrases required to generate general concepts for valid output tuples (_general concept-association-general concept_). We addressed this issue in [38] by introducing DnD transformation specifications to support conditional branching, which could be used to handle such exceptions and to apply different processing logic of different cases. Therefore, it is safe to conclude that model element naming practices do influence the actual performance of the transformations. At the same time, it should be mentioned that the impact of some of the issues could be minimized to a certain degree by  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Rule** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5**  
utilizing advanced NLP techniques [38]. Nonetheless, the obtained results indicate that the developed DnD transformations provided structurally correct results for most of the performed drag-and-drop actions with the selected UCM elements from \(S\). Moreover, several other observations were made after the experiment:  
* Drag-and-drop actions-based transformations proved to be useful and provide the required level of flexibility in both the visual development of transformations themselves as well as their actual application. In the future, the existing DnD transformations library could be further extended by adding additional draggable source elements to DnD specification classes referencing the already existing transformation patterns or the newly developed ones. This would allow a user to trigger transformations from an even larger set of draggable elements in the use case model. * We argue that the DnD transformation approach may not be the best choice for performing full-scale M2M transformation on large models. The reason is that it would take a reasonable amount of time to drag-and-drop every required source model element to acquire the full target model based on the provided source model. For the full-scale M2M transformation, it is advised to use the automatic or semi-automatic M2M transformation approach presented in [6]. The DnD transformation approach is better suited for the agile and incremental development of visual models and/or when the models under development are of a smaller volume. * The experiment provided a strong reassurance that our developed DnD transformation technology can be applied to the development of transformations for UML and other visual languages expressed via UML profiles. * It can be safely concluded that data quality is one of the determining factors which must be considered while applying this approach. No NLP tool will be able to handle invalid names or bad modeling practices despite its performance in other fields. * While NLP functionality was not considered in this paper, it can be useful to improve the semantic quality of the generated elements [8; 38]. Furthermore, this would allow enabling the transformation rules T\({}_{6}\) and T\({}_{20}\) that were described in this paper but excluded from our experimentation.