## 8 Conclusions  
In this paper, we present an NLP extension to user-interacted partial M2M transformations based on drag-and-drop actions. The previously introduced key features, emphasizing the customization, usability, and reusability of model-driven M2M transformations [14], were extended with NLP capability to enable advanced text processing of model element names and properties. This ensures that the outputs of M2M transformation are more representative of and aligned with the actual intent of transformation designers. This work presents an additional extension to the underlying visual language for modeling mapping patterns in M2M transformation specifications, mainly natural language-based operators, which can be used to develop M2M transformations with greater flexibility. Additionally, we refined our previously developed M2M transformation metamodel by introducing an extension that allows one to model conditional transformations for cases where more advanced transformation scenarios are required. As shown in our previous work as well as in this paper, our solution can be applied to any graphical modeling language implemented as a UML profile or the UML itself. The solution has already undergone experimentation with four modeling languages, UML, BPMN, SoaML, and SBVR, by using their UML profiles [15; 17; 18] implemented in the MagicDraw CASE tool. Again, we expect our approach to be easily portable to other visual modeling platforms that meet certain requirements due to the underlying abstract framework we developed. This framework also encompasses all of the extensions presented in the paper, therefore it could be used to build CASE tool-specific components for other MOF-based platforms as well. In this paper, we present several actual transformation use cases illustrating the usability of the proposed NLP-extended solution for designing and executing partial M2M transformations across different modeling languages. Obviously, this is not a finite number of all possible use cases; specialized libraries of such transformation specifications could be developed and customized by other practitioners working in this field. Evaluating recent NLP technologies based on state-of-the-art machine learning and deep learning techniques proved the potential of the proposed approach. The Stanford Stanza-based extractor was recognized as the leading tool and was used to implement the functionality offered in this paper. Nevertheless, we also conclude that data quality should be taken into account, as bad modeling practices or invalid names will not be processed correctly by any tool, irrespective of its performance in related fields. While this approach presents a significant amount of novelty, we already established several extension points for our future research. Extending condition specification language to support expressions of even greater complexity, including composite predicates and disjunctive and conjunctive clauses, is considered as one of the primary tasks; this is one of the basic requirements leading to the development of more advanced and comprehensive model-based transformation language. Condition resolution and elimination (or notification) of contradictions/overlaps in branching conditions would be another advanced feature contributing to future development. Also, more sophisticated transformations could be enabled by extending the existing transformation engine so that it could process deeper hierarchical levels of the designed transformation patterns. We are quite firm in our assumption that such extensions could lead to supporting full-scale M2M transformations and serve as a competitive alternative to existing model-wide M2M transformation solutions. P.D. : Conceptualization, Investigation, Software, Writing--original draft, Writing--review & editing. T.S. : Conceptualization, Investigation, Methodology, Writing--original draft, Writing--review & editing. R.B. : Investigation, Supervision. All authors have read and agreed to the published version of the manuscript.