The model term refers to a simplified representation of objects, processes or other subjects, and is used in the discipline of software ngineering to represent an abbreviated realism excerpt. The model transformation extends this area by the transfer of information between several models and is an integral part of modern software development, especially in the field of model-driven software development. This thesis deals with various possibilities for the detection of information loss in the field of model transformation. This is necessary to be able to ensure that information is transferred from a source, to a target model correctly, as well as to detect semantic differences between affected models. In the focus of this essay are the two research questions "Where does a model lose its information when transferring it to another model?" and "Has the semantics of a data set been changed by the transformation?". The first of the two research questions is the preservation of information. According to this, data should not be corrupted and should reach the correct position in the target model, whereas the second problem focuses on the recognition of model characteristics in which the affected models differ. This is about information that exists in the target model but not in the source model. To answer the two questions, fundamentals of modeling as well as theoretical concepts and approaches in the field of model transformation and verification are presented. Furthermore, two graph-based implementations are introduced, which allow the identification of model characteristics affected by information loss. These are, in particular, the approach of a graph-based constraint solver and a method for recognizing node patterns using a Neo4j graph database. In addition, the verification component of the used transformation framework is explained, which enables rudimentary model checks. Finally, the presented practical methods are evaluated using two examples. This evaluation compares the verification methods and results in various advantages and disadvantages, while also demonstrating the basic applicability of the implementations for the detection of information loss.
|Translated title of the contribution||Detection of information loss in the model transformation|
|Publication status||Published - 2019|