About our research
Research institute
The Master’s programme in Computing Sciences is offered in close collaboration with the Institute for Computing and Information Sciences (iCIS). The Software Science specialisation builds on the strong international reputation of iCIS in areas such model-based testing, virtual product development, advanced programming, and domain specific languages.
Institute for Computing and Information Sciences
Software Science department
The Software Science department at iCIS has expertise covering a broad range of topics concerning software construction and analysis. Our group is well-known for research on:
- model learning, model-based testing, and model checking
- program verification using proof assistants
- combining formal verification and machine learning
- model-based software engineering
- domain specific languages
- functional programming and HPC/array computing
- mathematical foundations of software: type theory, concurrency theory, co-algebras, and term rewriting
Student projects
A large part of this programme is focused on research. Examples of student projects are listed below. View more student projects at the Software Science department.
Markov decision processes are the model to describe sequential processes with stochastic uncertainty, e.g., to describe models with components that may fail. The probabilistic model checker Storm is a state-of-the-art model checker. In this direction, we implement and improve approaches from the literature to accelerate state-of-the-art model checking. In particular, we look at typical structures in MDPs and developed targeted algorithms.
This theme is coordinated by dr. Sebastian Junges.
Software-intensive systems constantly evolve. To prevent software changes from unintentionally introducing costly system defects, it is important to both understand how the current software behaves, and what impact changes have on that behavior. When changes are made, engineers often do not understand the (system-wide) impact of those changes. We therefore use model comparison techniques to compare models of the software versions before and after a change to find all behavioral differences.
This theme is coordinated by dr. Dennis Hendriks.
With the rapidly growing application of artificial intelligence and machine learning in social life, considering for instance autonomous systems such as self-driving cars, the need for verified or dependable guarantees against potentially fatal accidents is self-evident. An important question is then, how systems can be made safe. In this theme, you can do projects in various directions, such as Neural Network Robustness, Decision-Making under Uncertainty, Human-Robot Interaction, and Model Learning.
This theme is coordinated by dr. Nils Jansen.
Research projects
Curious what our researchers are working on? View ongoing research projects below.
