We currently offer bachelor and master theses in the following research domains:

Explainable software systems

As intelligent systems are becoming increasingly more advanced, their internal process and operations become less compressible by users. However, to establish a successful interaction between humans and an intelligent system, people need to trust the fairness, correctness, and usefulness of actions performed and decisions made by such a system. 

To address this need, an intelligent system (if it is intelligent enough!) should be able to explain the causes, motivations, and benefits behind its actions and decision in a format that is easily understandable by ordinary users. 
Explainable intelligent system is a research domain that investigates the techniques, methods, and tools to make the intelligent system interpretable and comprehensible.

Requirements Engineering

Requirements engineering, as the first phase of a development project, is decisive for the success of the project, since the further course of the project is largely determined by this phase. In this phase, the project participants and their goals are identified, any conflicting goals are resolved, and requirements for the system to be developed are formulated. The formulated requirements also become the basis of the contract between the client and contractor and the criteria for the acceptance of the finished system.

We are interested in empirical studies and tool support for RE activities. We apply Natural Language Processing, Machine Learning, and Statistical Measures to make the lives of requirements engineers easier.

Software Engineering for AI

In this research domain, we deal with the challenges in software engineering for the development of software systems that are heavily based on machine learning or AI. We are interested in how an idea and an initial model developed by a data scientist (e.g., scripts and Jupyter notebooks) can be used as part of a scalable and maintainable system (e.g., mobile apps, web applications, IoT devices). We do not focus on modeling and learning per se, but rather on issues of design, implementation, operation, and security and how these interact with the data scientist's modeling.

 

 

How to apply?

If you are interested in writing a thesis, please consider the following points:

1. If you already have an idea for a topic of your interest: Note that we prefer theses that are closely related to our research areas (see above). In any case, you are welcome to send an e-mail (German or English) to thesis-ssecs.uni-koeln.de with
   1. a brief overview of the specialization you are interested in,
   2. a reason why you consider mentoring by SSE to be particularly promising, and
   3. a short curriculum vitae, including a recent overview of your grades.
2. If you have already participated in courses of our chair, but have not yet found a specific topic that interests you: Please do not hesitate to send an e-mail (German or English) to thesis-ssecs.uni-koeln.de with
   1. A list of the three courses you have enjoyed the most in your course of study so far,
   2. a scientific publication that you find interesting and that has something to do with our research interests, and
   3. a short curriculum vitae, including a recent overview of your grades.