An Aggregated Dataset of Agile User Stories and Use Case Taxonomy for AI-Driven Research

Agile methodologies are considered revolutionary approaches in the development of systems and software. With the rapid advancement of artificial intelligence, natural language processing, and large language models, there is an increasing demand for high-quality datasets to support the design and development of intelligent, practical, and effective automation tools. However, researchers in Agile Requirements Engineering face significant challenges due to the limited availability of datasets, particularly those involving user stories. This paper presents a dataset of over 10K user stories collected from academic sources and publicly accessible online repositories. These stories represent requirements formulated in accordance with Agile principles. The process of collecting and classifying data, as well as its use in a prior research project focused on identifying non-functional requirements, is described. The dataset was validated with substantial inter-annotator agreement and has been successfully employed in prior experiments, where a fine-tuned pre-trained language model achieved F1 scores above 93% in classifying non-functional requirements. Additionally, a structured taxonomy of potential research and practical use cases for this dataset is proposed, aiming to support researchers and practitioners in areas such as requirements analysis, automated generative tasks using generative language models, model development, and educational purposes.