Cited By
View all- Xiao YZuo XLu XDong JCao XBeschastnikh I(2025)Promises and perils of using Transformer-based models for SE researchNeural Networks10.1016/j.neunet.2024.107067184(107067)Online publication date: Apr-2025
Finding appropriate user feedback analysis techniques for multiple data domains
Pages 316 - 318
Abstract
Software products now have more users than ever. This means more people to please, more use-cases to consider, and more requirements to fulfill. These users can then write feedback on software in any number of public or private online repositories. Many tools have been proposed for classifying, embedding, clustering, and characterizing this feedback in aid of generating requirements from it. I am investigating which techniques and machine learning models are most appropriate for enabling these analyses across multiple feedback platforms and data domains.
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- Finding appropriate user feedback analysis techniques for multiple data domains
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Published: 19 October 2022
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Cited By
View all- Xiao YZuo XLu XDong JCao XBeschastnikh I(2025)Promises and perils of using Transformer-based models for SE researchNeural Networks10.1016/j.neunet.2024.107067184(107067)Online publication date: Apr-2025
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