Sebastian Stüber, M.Sc.

Field of Work

• Formal Verification of Distributed Systems
• Semantic Difference of Models
• Compositional Analysis of Systems
• Model Driven Development
• Software Architectures

Projects

• SemanticDiff: Semantic differencing is a method used in model-based software development to compare two different models and identify the elements in the first model that are not present in the second model. This approach helps us understand the changes made to the original model to create a newer version. By revealing the semantic differences, we can gain insight into the impact of the syntactic modifications on the overall meaning and functionality of the model. This analysis is crucial because it allows developers to comprehend the effects of their changes and ensure that the updated model aligns with the desired objectives. Ultimately, semantic differencing aids in enhancing the quality and effectiveness of model-based software development processes.
• FeCoMASS: Compositional analysis is an important aspect of model-driven engineering that aims to provide more flexibility in analyzing complex systems. It involves breaking down a system into smaller components and analyzing them individually to gain a better understanding of the overall system behavior. This approach is crucial because as systems become more heterogeneous and complex, traditional analysis techniques may not be sufficient to accurately assess their performance and behavior. By decomposing the system and analyzing its components separately, we can ensure sound analysis results and also enable the flexible use and reuse of model-based analyses. This means that the analysis techniques can be easily configured and adapted to different scenarios, making it a valuable tool for engineers and researchers working on complex systems. The FeCoMASS project aims to investigate the foundations of compositional analysis to develop more efficient and effective analysis techniques for the systems of tomorrow.
• Agile Data Development: The “Agile Data Dev” project aims to develop a guideline for implementing an agile development process that integrates both agile and plan-driven methods, with a focus on data-based support. The project aims to achieve several goals: (1) increase effectiveness by addressing requirements in an agile manner, (2) improve efficiency by avoiding unnecessary iterations through improved data transparency, (3) enable data-based decision support through an integrated data and system architecture, and (4) reduce time-to-market while increasing market share. This project is important because it aims to increase the overall profitability of the windmill portfolio in the medium to long term, improve efficiency in developing new plants, and reduce costs of parts, components, and modules used across projects. The project is part of MontiGem, which aims to generate enterprise management systems.
• MontiBelle: The “MontiBelle” project aims to reduce certification costs for critical systems such as intelligent flight control or traffic management systems. By applying mathematical dataflow theory, knowledge representation, and intelligent reasoning to software engineering, the project uses the theoremprover Isabelle to perform correctness proofs or find counterexamples. To make Isabelle more user-friendly, the project provides a high-level API and a domain-specific modeling language as a front end, along with code generator to Isabelle. This project is important because it has the potential to greatly reduce the costs associated with certifying critical systems, making them more reliable and safer for use in areas such as air and road traffic management.
• Energy Information System: This project aims to visualize data collected by sensors in buildings, such as temperature measurements. The project team has developed a web interface that presents this data in an easily understandable format, making it more accessible to users. By providing a visual representation of the information, the project facilitates a better understanding of energy usage in buildings. This is crucial because it enables users to monitor and manage energy consumption effectively, leading to more efficient energy usage and potential cost savings.
• Big Data in Building Automation: The aim of the project is to develop and evaluate methods for applying Big Data analysis methods to building automation data in order to identify potentials for optimizing the energy operation of existing buildings. The innovative focus lies in the application of analysis methods for large and complex data sets (Big Data) to the enormous amount of operating data of building and component automation in modern buildings. Today, these are generally only used for the direct operational management of the buildings and systems. Only a minimal amount of data is used for visual inspections, alarms, or the most basic analysis and reporting. Most data is not stored or evaluated. The approach taken here is to systematically apply powerful Big Data methods, in particular through visualization/mapping and algorithms for data analysis, to historicized and real-time data from building automation systems and individual building technology components such as heat pumps, boilers, ventilation units or pumps, in order to analyze the potential of this data and develop utilization concepts.

Teaching

• SoSe23: Lecture “Software Language Engineering” Studentproject “GuiDSL to Flutter - Model Driven Development for all Devices”
• SoSe22: Lecture “Software Language Engineering” Studentproject “Analysis of MontiArc Automata”
• SoSe22: Seminar “Applying Formal Methods in Software Engineering”
• SoSe21: Teaching Assistant “Software Architectures”
• WiSe20/21: Practical Course “Software Engineering for Connected Vehicle Platforms”
• SoSe20: Teaching Assistant “Software Architectures”
• WiSe19/20: Seminar “Generated Information Systems”
• WiSe19/20: Two lectures of “Innovationen im Software Engineering”

Publications