| 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.
|
