B.Sc. Thesis
Behavior-Driven Development in the Age of AI-Assisted Programming | Current Topics
Context
Behavior-Driven Development (BDD) is a software development approach that uses structured, natural-language specifications (typically written in Gherkin language) to describe system behavior through concrete examples and scenarios. These specifications support shared understanding between developers, testers, and domain experts and can be directly linked to automated tests.
With the rise of “vibe coding” and Large Language Models (LLMs), software development is increasingly driven by informal prompts and rapid prototyping. While this enables fast development, it often lacks systematic specification and traceability. BDD offers a structured way to describe expected behavior and may serve as a high-quality input for AI-based code generation.
This project is conducted in collaboration between multiple universities (FHNW, the university of Sannio, Italy) and investigates how BDD practices can be combined with modern LLM-based development.
Feature Engineering for Classification-Based Merge Conflict Resolution | Current Topics
Context
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.
RL-based Training for Code in LLMs | Current Topics
Context
Large Language Models (LLMs) have shown strong performance in code generation, completion, and repair tasks. However, supervised pretraining on massive code corpora is limited by data quality, lack of explicit feedback, and the inability to capture correctness beyond next-token prediction. Recent research has explored Reinforcement Learning (RL) based training approaches to refine LLMs for code. By leveraging feedback signals—such as compilation success, test case execution, or static analysis warnings—models can be trained to better align with correctness and developer intent.
Trust-VR: Feasibility of AI Architectures for Virtual Pediatric Patients | Current Topics
Context
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.