Intelligent Model Management based on Textual and Structural Extraction-An Exploratory Study

The wide range of models employed in many categories is constantly growing due to advancements in Model-Based System Engineering and Model-Driven Engineering, as well as their acceptance in academic and industry communities. Consequently, intelligent approaches have to be developed to manage these models, including comparing, clone detection, analyzing, and searching for similarity. There are obstacles and restrictions when using machine learning algorithms on models because of their special structure. While these models comprise graph structures, they also contain textual information. Both of these features must be considered to perform more accurate learning. This paper explores the challenges associated with this area of study and discusses potential solutions. Since comparison is the basis of many learning algorithms, it is imperative that the data in this category be prepared in a fashion suitable for comparison. In this sense, the text and structural aspects of embedding meta-models (as one of the popular techniques in the model's world) techniques are assessed, and an approach for combining both aspects is suggested. Studies in this area have been carried out using evaluation criteria including goals, base method, efficiency, accuracy, and scalability. The outcomes indicate a text-structure combination can work well as an embedding model for more investigations.