ארנון שטורם

User stories are a popular notation for representing requirements, especially in agile development. However, how they can be used further during the development process is still under examination. In recent years, user stories research has focused on how to apply machine learning to user stories to understand their quality, the required implementation efforts, and the ability to generate software artifacts. One primary concern in this direction is the lack of datasets and specially labeled ones. To bridge this gap, in this work, we introduce a framework for tagging user stories to construct labeled datasets for user stories for various tasks.