Keywords: modeling tools, concurrent editing, editing capabilities, model management, web-based editors
Astract: Currently, many modeling toolchains are moving from monolithic desktop applications to cloud-based solutions.The involved editors of those tools should be operating independently from one another, which means direct communication between those services is not allowed. This is because, that those micro services should stay reusable and extensible.However, at the same time these independent editors and their supporting micro services should operate on common models and allow to manipulate potentially overlapping parts of the underlying models.This thesis aims to solve this problem, by introducing an independent component, that is able to manage the entire model handling, whilst being centralized and easily accessible.It also has to ensure the interoperability of multiple editors used by one user at a time to manipulate an underlying model.The requirements for such an artifact are researched and defined in the scope of this thesis.To demonstrate the artifact, editing capabilities that work with the newly introduced model management component are implemented.Finally, to evaluate the artifacts, the managing component as well as the editing components are integrated in a web-based editor project which is directly comparable to an existing desktop application.

Keywords: Internet of Production, Industry 4.0, Digital Shadow, Digital Twin, Smart Production

Keywords: Acceptance tests, Distributed systems, Selenium, Software testing, Web testing
Astract: Acceptance tests are used to ensure that a software system delivers its required functionality. They are performed on a system prior to its delivery to a live environment. Acceptance testing becomes a complex task when the system under test (SUT) is distributed, i.e. it consists of multiple applications interacting with each other. The main challenge lies in the synchronization and administration of the test process. Automating acceptance tests of such systems can result in high effort when not performed in a structured way. Control center solutions for handling and managing emergency calls are highly distributed and complex systems. They are usually embedded into an existing infrastructure of services (like telephony call lines, radio communication systems and 3rd party clients). Hence, automating acceptance tests for control center solutions requires simulation on multiple interfaces. As no suitable tool is available for testing control center solutions, Frequentis developed an acceptance test automation framework. With this framework, it is possible to control all necessary communications with the SUT in a uniform and simple way. However, it is only capable of addressing Java Swing based front-ends. As more and more applications become accessible via web interfaces, Frequentis’ control center solutions also offer web based clients to enable emergency call handling via a browser. Therefore the existing test automation framework is required to also address web front-ends. In this master thesis, we extended this framework so that web front-ends participating in a distributed system can be addressed and tested. We collected the necessary requirements, designed and implemented a solution which integrates into the existing framework. This framework extension offers an API to encourage test developers to apply the page object pattern in order to create maintainable tests. Our extension is based on the browser automation tool Selenium. We used requirements engineering to determine the necessary capabilities of the extension. Furthermore, we performed evolutionary prototyping to develop and integrate the extension into the existing framework. Eventually, we performed a case study to evaluate whether the extended test automation framework fits its intended purpose. Our study shows that the extension is used in numerous projects within Frequentis and that there is a high adoption rate of the offered API.

Keywords: Theia, EMF Compare, Diagram diff merge, Cloud based IDE
Astract: The last decade has brought about countless advancements in the field of technology. Where once software development was a toilsome process done in plain text editors, lacking any sort of syntax highlighting, validation or other means of aiding the developer, now IDEs and specially designed code editors reign supreme in this field. Long gone are the days when building and running even the simplest pieces of software, required the developer to undertake a series of tasks. Now most of these tasks can be achieved through the push of a button in the IDE. Be it called "Run", "Execute" or "Start", the overall functionality of that button is clear: turn the code from text in an editor to a running program that achieves its goal. Aside from the usability aspect, the last few years have seen improvements in other areas, with formerly niche methodologies becoming more and more mainstream, such as Model-driven development, or MDD for short. Furthermore the emergence of cloud-based IDEs means that developers are no longer bound by the computational capabilities of the machines that are physically available to them. The client-server architecture of such IDEs allows devices with modest specifications to delegate the more intensive tasks to a server and merely concern themselves with sending the instructions and displaying the results. It is at the intersection between the fields of cloud-based IDEs and MDD where this thesis makes its contribution. MDD allows developers to build complex applications while maintaining a high level of abstraction, and to quickly make changes without concerning themselves with code-specific issues. Because complex applications often have correspondingly complex models behind them, it is often the case that more than one developer will be working on a model and/or diagram of a system. This raises the need for collaborative modeling support for diagram-based representations, in a similar way to the text-based support offered by tools such as Git or SVN, which allow different versions of a file to be compared and merged with each other. This thesis analyzes the available support for diagram diffing and merging both within the most commonly used cloud-based IDEs, as well full-fledged IDEs, and provides an implementation of its own for the Theia cloud-based IDE. Already existing, and well established components and frameworks have been reused wherever suitable, thus presenting users with somewhat known diff visualization means, such as side by side comparisons, as well as merging mechanisms. The computation of the comparison as well as the merging process are being handled by the EMF Compare framework, with the focus of this thesis lying on the visualization of the diffs and means of conflict resolution and diagram merging. The results have been evaluated in regard to the efficiency of the protocol facilitating the communication between the frontend and the backend components of the implementation, as well as in regard to the usability of the developed visualization and merging / conflict resolution means. This evaluation has been conducted by way of benchmarking in case of the communication protocol while the System Usability Scale has been used to measure the usability of the implementation. The results themselves show that the developed diffing and merging capabilities were easy to use and sufficient for daily tasks for a majority of respondents, scoring between good and excellent on the System Usability Scale.

Keywords: Model Driven Engineering, Model Engineering, Pattern Recognition, Machine Learning, Clustering, Dual Deep Instantiation, Sensor Systems, Model Intelligence, Model Generation, Monitoring Systems
Astract: This thesis examines the possibility of model generation from data sets. Automatic model generation from data sets is a subtopic of Model Intelligence. Model Intelligence aims to combine the benefits of Model Driven Engineering (MDE) with the benefits of machinelearning (ML) techniques. During a workshop about Model Intelligence a call for paper,namely "Model inferencers and automatic model generators from datas ets" [ MDE ]. In this work we propose a method to generate models with the help of machine learning from data sets and make a case study on a real-life data set coming from a sensor system of the (Plus-)Plus-Energy building of the Vienna University of Technology. Different definitions of"model" are considered to answer the question whether or not it is possible to generate models automatically from sensor data with the help of machine learning and data mining techniques.A frequent pattern recognition based method is introduced, which aims to generate a frequent pattern based model for sensors. To set boundaries for model generation, we discuss the basic concepts and introduce key techniques of metamodeling. We choose a suitable technique to define our metamodel, which then will serve as the basis of model generation. During defining the model and exploring different modeling techniques we mainly discuss methods and related concepts conforming to the Meta-Object Facility (MOF).After defining the model, we apply segmentation on the sensor-derived data set, and label different segments according to the Symbolic Aggregate approXimation (SAX) We apply frequent pattern mining on the segmented and labeled data sets. Based on the results of the patternmining, we carry out clustering to help us decide, whether the frequent patterns found in the sensor data are suitable to differentiate between different types of sensors, thus can serve as attributes in model definitions for the different types of sensors.Our results show that depending on what definition of "model" is used, it is possible to generate models either on the M1 or the M0 level of the M4 meta-modeling framework. Furthermore, aproposed model with Double Deep Instantiation (DDI) is feasible to model type definitionsfor the sensors. In conclusion, we show that generating type definitions for the sensors based on their frequent patterns is successful in most cases. We have found some instances with interesting caveats that call for further investigation.ix