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.
While standard code models generate text one token at a time (autoregressive), Diffusion Language Models (DLMs) generate and refine the entire block of code simultaneously. This allows the model to look ahead and fix structural errors in a non-linear fashion, where inference becomes an online optimization problem.
Over the last few years, the usage of large language models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI has increased, as has the quality of the generated outputs. While RAG enables LLMs to leverage (internal) company information to answer more complex and detailed questions, we face the issue that this information is not necessarily well-structured, centralized, or available in high quality (e.g., a significant amount of knowledge resides in emails, where the information is distributed across multiple conversations, folders, and mailboxes). Therefore, the quality of answers produced by RAG systems strongly depends on the quality of the available information.
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.