  Mobile applications (apps) are often developed by only a small number of
developers with limited resources, especially in the early years of the app's
development. In this setting, many requirements acquisition activities, such as
interviews, are challenging or lower priority than development and release
activities. Moreover, in this early period, requirements are frequently
changing as mobile apps evolve to compete in the marketplace. As app
development companies move to standardize their development processes, however,
they will shift to documenting and analyzing requirements. One low-cost source
of requirements post-deployment are user-authored scenarios describing how they
interact with an app. We propose a method for extracting use case components
from user-authored scenarios using large language models (LLMs). The method
consists of a series of prompts that were developed to improve precision and
recall on a ground truth dataset of 50 scenarios independently labeled with UC
components. Our results reveal that LLMs require additional domain knowledge to
extract UC components, and that refining prompts to include this knowledge
improves the quality of the extracted UC components.
