Our team focuses on basic research topics in both, symbolic and subsymbolic AI which are primarily motivated by industrial challenges. The research focus of our Applied Software Engineering & AI research group (ASE) can be summarized as follows:

XAI Approaches in Recommender Systems: Explainabile AI is based on the idea of intelligent explanation concepts that make recommendations transparent for different stakeholders part of a recommendation process. Application in different domains such as Software Engineering (e.g., release planning), Medicine (e.g., medical treatments), and Financial Services (e.g., recommendation of loans).

Recommender Systems: development of machine learning techniques that help to predict preferences of users in diverse application scenarios such as Financial Services, Intelligent Video Segmentation, E-learning, and Software Engineering (e.g., Requirements Engineering). A specific research focus of our team is to analyze the potentials of recommender systems in achieving the 17 Sustainable Development Goals (SDGs) defined by the United Nations.

Configuration Systems: logic-based and machine learning based AI techniques for efficient solution search in constraint-based reasoning scenarios and beyond (e.g., model-based diagnosis and conflict detection). Application in different domains such as automotive, software engineering (e.g., release planning and open source development), and Internet of Things (IoT).

Requirements Engineering: development of machine learning approaches to improve the quality of Software Engineering processes. Examples thereof the automated dependency detection in Requirements Engineering, intelligent Release Planning, and Requirements Recommendation (e.g., for reuse purposes). A specific focus is the exploitation of large language models (LLMs) to boost the quality of Software Engineering processes – this also includes aspects such as gender awareness and fairness.

Psychology-Aware Artificial Intelligence: a major focus of our research group is the analysis and integration of theories and methods from cognitive psychology into the development of software systems. Examples thereof are: persuasive technologies for software development environments, decision-bias aware recommendation technologies, and psychologically-aware recommendation technologies for e-Learning scenarios.

AI methods for Intelligent Learning Support: in this context, we are focusing on the development of intelligent AI technologies for counteracting forgetting, i.e., machine learning based approaches that help to tackle the challenges imposed by forgetting curves.