Institutsseminar/2024-01-19: Unterschied zwischen den Versionen
Keine Bearbeitungszusammenfassung |
Keine Bearbeitungszusammenfassung |
||
| Zeile 2: | Zeile 2: | ||
|datum=2024-01-19T11:30:00.000Z | |datum=2024-01-19T11:30:00.000Z | ||
|raum=Raum 010 (Gebäude 50.34) | |raum=Raum 010 (Gebäude 50.34) | ||
|online=https://sdq.kastel.kit.edu/ | |online=https://sdq.kastel.kit.edu/institutsseminar/Microsoft_Teams | ||
}} | }} | ||
Aktuelle Version vom 11. Dezember 2023, 01:23 Uhr
| Datum | Freitag, 19. Januar 2024 | |
|---|---|---|
| Uhrzeit | 11:30 – 12:15 Uhr (Dauer: 45 min) | |
| Ort | Raum 010 (Gebäude 50.34) | |
| Webkonferenz | https://sdq.kastel.kit.edu/institutsseminar/Microsoft Teams | |
| Vorheriger Termin | Mo 1. Januar 2024 | |
| Nächster Termin | Fr 2. Februar 2024 |
Termin in Kalender importieren: iCal (Download)
Vorträge
| Vortragende(r) | Elias Hofele |
|---|---|
| Titel | Identifying Security Requirements in Natural Language Documents |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Sophie Corallo |
| Vortragssprache | |
| Vortragsmodus | in Präsenz |
| Kurzfassung | The automatic identification of requirements, and their classification according to their security objectives, can be helpful to derive insights into the security of a given system. However, this task requires significant security expertise to perform. In this thesis, the capability of modern Large Language Models (such as GPT) to replicate this expertise is investigated. This requires the transfer of the model's understanding of language to the given specific task. In particular, different prompt engineering approaches are combined and compared, in order to gain insights into their effects on performance. GPT ultimately performs poorly for the main tasks of identification of requirements and of their classification according to security objectives. Conversely, the model performs well for the sub-task of classifying the security-relevance of requirements. Interestingly, prompt components influencing the format of the model's output seem to have a higher performance impact than components containing contextual information. |
- Neuen Vortrag erstellen