Automating feedback analysis to support requirements relation and usage understanding [data] - Anders, Michael (Universität, 2025)
Contains all relevant data for the dissertation "Automating Feedback Analysis to Support Requirements Relation and Usage Understanding". ReadMe are provided for each section of dataset. Software development often faces a gap between developers' assumptions and users' real needs. While direct user involvement is valuable, it is often impractical, making online user feedback a crucial but challenging resource due to its unstructured nature. This dissertation addresses two main challenges: identifying which functionalities users discuss in their feedback and understanding how users interact with them. To tackle these, two machine learning-based approaches are proposed: one relates user feedback to existing software requirements, and the other extracts detailed usage information using the TORE framework. Following a Design Science methodology, the thesis includes systematic mapping studies, the design and evaluation of automatic classifiers, and the development of a supporting software prototype, Feed.UVL, along with a Jira plugin to integrate into existing workflows. The contributions include new methods for feedback analysis, evaluated classifiers, annotated datasets, and insights into current research in the field.
Gesehen am 18.06.2025
Keywords: Forschungsdaten; Datenbank