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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....

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Bibliographic Details
Main Author: Anders, Michael (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2025-04-17
DOI:10.11588/DATA/RTCGSG
Subjects:
Forschungsdaten
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Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.11588/DATA/RTCGSG
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/DATA/RTCGSG
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Author Notes:Michael Anders
Description
Summary: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.
Item Description:Gesehen am 18.06.2025
Physical Description:Online Resource
DOI:10.11588/DATA/RTCGSG

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