Below, you will find current topics that can be worked in the context of a Seminar Software Engineering, a Bachelor’s or a Master’s Thesis. The context indicates the scope of the work, and the keywords give you further information about the topic and its domain.
Mind that there are multiple pages, you can navigate them using the buttons on the bottom.
Merge conflict resolution remains a significant challenge in Git-based software development, as manual conflict resolutions slow down collaboration and reduce developer productivity. However, empirical research results suggest that a vast majority of chunk resolutions found in practice can be derived from a fixed set of conflict resolution patterns, combining the ours, theirs, and base parts of a conflicting chunk in a pre-defined way. These findings form the foundation for phrasing merge conflict resolution as a classification problem, and thus using traditional machine learning for predicting conflict resolutions.
Merge conflict resolution remains a significant challenge in Git-based software development, as manual conflict resolutions slow down collaboration and reduce developer productivity. However, empirical research results suggest that a vast majority of chunk resolutions found in practice can be derived from a fixed set of conflict resolution patterns, combining the ours, theirs, and base parts of a conflicting chunk in a pre-defined way. These findings form the foundation for phrasing merge conflict resolution as a classification problem, and thus using traditional machine learning for predicting the correct resolution.
Managing distressed pediatric patients in clinical environments is a challenging yet critical skill for healthcare professionals. Patients exhibit diverse emotional responses—ranging from anxiety and shyness to outright resistance—making it essential for clinicians to adapt their approach. Traditional training methods often lack the realism and variability needed to prepare professionals for these high-stakes interactions, particularly when it comes to emotional and behavioral dynamics in children.
This project operates within the domain of agentic AI, information retrieval, and research methodology automation. Systematic literature reviews (SLRs) are fundamental to evidence-based research, yet their execution remains largely manual and time-intensive. The project builds on an existing paper collection system that queries academic databases (arXiv, Semantic Scholar, DBLP) using structured configurations, and aims to transform it into a multi-agent pipeline where researchers provide a natural language topic description and receive a draft survey report with human oversight at critical decision points.
Autonomous vehicles (AVs) are complex cyberphysical systems that require extensive validation to ensure safety and reliability. Since real-world testing is expensive and potentially unsafe, simulation-based testing using platforms like CARLA has become a key component of AV software validation. These simulators reproduce realistic traffic scenarios, sensors, and environmental conditions, but they are computationally intensive and consume significant amounts of energy. As large-scale simulation campaigns become common (e.g., thousands of tests in continuous integration pipelines), improving the energy efficiency of simulation-based testing becomes increasingly important for sustainable software engineering.