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  4. A practical user feedback classifier for software quality characteristics
 
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2021
Conference Paper
Title

A practical user feedback classifier for software quality characteristics

Abstract
It is common practice for users to provide feedback on apps through social media or app store reviews. This feedback is a rich source of requirements for these apps. However, manually analyzing vast amounts of user feedback is unfeasible, so automated user feedback classifiers are useful tools. This research work presents a user feedback classifier based on Machine Learning (ML) for the classification of reviews according to software quality characteristics complaint with the ISO25010 standard. We developed this approach by testing several ML algorithms, features, and class balancing techniques for classifying user feedback on a data set of 1500 reviews. The maximum F1 and F2 scores obtained were 60% and 73%, with recall as high as 94%. This approach does not replace human specialists, but reduces the effort required for requirements elicitation.
Author(s)
Santos, Rubens dos
Villela, Karina
Fraunhofer-Institut für Experimentelles Software Engineering IESE  
Avila, Diego Toralles
Thom, Lucineia Heloisa
Mainwork
33rd International Conference on Software Engineering & Knowledge Engineering: Technical Program, SEKE 2021. Proceedings  
Conference
International Conference on Software Engineering & Knowledge Engineering (SEKE) 2021  
DOI
10.18293/SEKE2021-055
Language
English
Fraunhofer-Institut für Experimentelles Software Engineering IESE  
Keyword(s)
  • Balancing

  • Computer software selection and evaluation

  • Engineering research

  • Statistical tests

  • Software quality

  • User feedback

  • Ml algorithms

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