Institutsseminar/2026-05-22-Room010
| Datum | Freitag, 22. Mai 2026 | |
|---|---|---|
| Uhrzeit | 14:00 – 14:45 Uhr (Dauer: 45 min) | |
| Ort | Room 010 (building 50.34) | |
| Prüfer/in | Anne Koziolek | |
| Webkonferenz | ||
| Vorheriger Termin | Fr 24. April 2026 | |
| Nächster Termin | Fr 12. Juni 2026 |
Termin in Kalender importieren: iCal (Download)
Vorträge
| Vortragende(r) | Lorenz Moser |
|---|---|
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Raziyeh Dehghani |
| Vortragssprache | Englisch |
| Vortragsmodus | in Präsenz |
| Kurzfassung | In modelling-heavy software-engineering projects, the relevant knowledge to understand the system is fragmented across heterogeneous source types such as research papers, project documentation, and source code. Vitruvius is one such project: an open-source framework for view-based software development and the technical core of the Convide Collaborative Research Centre. This fragmentation makes the onboarding of new users expensive. Therefore, this thesis develops a Retrieval-Augmented Generation (RAG) based chatbot for the Vitruvius domain in two variants: Naive RAG retrieves purely on semantic similarity, while Graph-Retrieval-Augmented Generation (GraphRAG) additionally exploits structural dependencies in the project’s knowledge base. The contributions are a knowledge organisation pipeline that handles each source type with its own chunking and graph- construction strategy, and the two retrieval variants built on it. To evaluate both variants, this thesis further contributes a 33-question Vitruvius-specific evaluation dataset and a joint evaluation combining a technical sweep, two pilot user studies with Vitruvius users (𝑛 = 5), and an alignment analysis between automated metrics and user perception. The identified Pareto-best GraphRAG configuration improves the mean of all retrieval metrics, such as recall@k and precision@k, by 4–21% over Naive RAG, The user study confirms that the sources retrieved by GraphRAG are more relevant: 3.2 to 2.9 on a 5-point Likert-scale. However, the retrieval-side advantage over Naive RAG comes with a 75% increased end-to- end latency and does not translate into measurably better generated answers. The alignment analysis only shows a weak correlation across all covered metric pairs (𝜌 < 0.20). |
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