Automating Feedback Analysis to Support Requirements Relation and Usage Understanding [data]

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.