Project Aicher

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.

Model transformation is a relevant part in modern software development. There are a few new interesting topics rising, especially in the area of Model-Driven Development (MDD). This thesis focuses on the development of a graph transformation framework. Part of the thesis is a theoretical analysis of the basics of model transformation. That does not only contain different types of model transformation, but also the definition of models and meta models. Moreover, theoretical concepts such as Triple Graph Grammar (TGG) will be analysed. Furthermore, big frameworks like Atlas Transformation Language (ATL) and Epsilon Transformation Language (ETL) are going to be explained. Apart from the already known concepts, a new concept will be introduced, about how a graph database like Neo4j can be combined with model transformation or with a model transformation framework. The core of the thesis is the design and implementation of the graph transformation framework. The different concepts, used design patterns as well as the analogies to the previously analysed frameworks are going to be discussed. The framework is going to be analysed by two different scenarios. The first one is the transformation of a graph into an XML representation. The second one is part of a research project, where a transformation between a 2D representation and a 3D representation of a building plan was created. Finally, the advantages as well as disadvantages of the frameworks are mentioned and an outlook to further work is given. This is going to show that especially the independence of other frameworks as well as the loose coupling to other frameworks is one of the major advantages of the framework. Nevertheless, it will also show, that a lot of functionality that is provided by other frameworks is missing. Even though some functionality of other frameworks is associated with a big overhead.

The analysis of building plans with regard to accessibility or problems of escape routes is a difficult task. The PASS project (Plan Analytics using Self learning Solutions) therefore develops analyses of building plans that have previously been transferred from a 2D building plan to an interim model. These analyses include a validation of accessibility, a simulation-based analysis of escape routes, and the optimal placement of furniture in rooms using machine learning algorithms.