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Temporal dynamics of requirements engineering from mobile app reviews
Vitor Mesaque Alves de Lima1, Adailton Ferreira de Araújo2, Ricardo Marcondes Marcacini1,2
1Faculty of Computing (FACOM), Federal University of Mato Grosso do Sul (UFMS), Campo Grande, Mato Grosso do Sul, Brazil.
Peerj. Computer Science
|May 2, 2022
Summary
This study introduces a method to predict defective requirements from app reviews before they impact app ratings. By analyzing temporal trends in user opinions, software engineers can proactively address issues for better app maintenance.
Area of Science:
- Software Engineering
- Natural Language Processing
- Data Mining
Background:
- Opinion mining from app reviews aids requirements engineering.
- Existing methods focus on corrective maintenance, overlooking preventive strategies.
- Analyzing large volumes of app review data manually is infeasible.
Purpose of the Study:
- To predict emerging trends in defective requirements from user opinions.
- To enable preventive software maintenance by identifying issues early.
- To understand the temporal dynamics of requirements in app reviews.
Main Methods:
- Developed the MAPP-Reviews (Monitoring App Reviews) method.
- Extracted requirements with negative evaluations from app reviews.
- Generated time series of negative evaluation frequencies.
- Trained predictive models to identify escalating negative requirement trends.
Main Results:
- User opinions in reviews contain predictive information about future requirement behavior.
- Identified requirements with high trends of negative evaluation.
- Demonstrated the effectiveness of the MAPP-Reviews method on ~85,000 reviews.
Conclusions:
- Temporal analysis of app reviews enables proactive identification of potential requirement defects.
- The MAPP-Reviews method supports preventive software maintenance.
- Anticipating requirement issues can improve future app ratings and user experience.
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