Institutsseminar/2024-09-13

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Vorträge

Vortragende(r)	Nathan Hagel
Titel	LLM4DSLs
Vortragstyp	Masterarbeit
Betreuer(in)	Sebastian Weber
Vortragssprache	Englisch
Vortragsmodus	in Präsenz
Kurzfassung	The steady rise of generative AI and large language models in software development raises the question of its applicability in model-driven engineering (MDE). Especially when using textual domain-specific languages (DSL) for modeling, insights into the domain itself and the internals of the DSL are required to create adequate models. To tackle this challenge, we present an updated MDE process aided by LLMs, which removes the necessity of manually creating textual models / writing DSL code manually. Additionally, we implemented tooling to use the updated process in practice. The process is evaluated in a user study and shows that it is faster and more efficient than traditional MDE. Furthermore, although not significantly better rated in perceived usability, 15 out of 18 participants still preferred the new process. Our results indicate that MDE is a good application for LLMs and can benefit from its capabilities.

Vortragende(r)	Yavuz Karaca
Titel	Validating Completeness in Software Requirements Specifications using Large Language Models
Vortragstyp	Bachelorarbeit
Betreuer(in)	Lars König
Vortragssprache	Englisch
Vortragsmodus	in Präsenz
Kurzfassung	This thesis explores the use of Large Language Models (LLMs) for validating the completeness of Software Requirements Specifications (SRS). Traditional SRS validation is a manual, time-consuming process that often results in missed requirements, potentially leading to project failures. This study leverages LLMs to automate the identification of incompleteness within SRS documents, focusing on completeness types defined by IEEE standards and Kuchta. The effectiveness of GPT-4 and LLaMA3 in identifying instances of incompleteness was evaluated through a series of experiments using different datasets and various prompting techniques. The F1 score was employed as the primary metric to assess performance.