RoseMatcher: Identifying the impact of user reviews on app updates

Tianyang Liu, Chong Wang*, Kun Huang, Peng Liang*, Beiqi Zhang, Maya Daneva, Marten van Sinderen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
14 Downloads (Pure)

Abstract

Context: The release planning of mobile apps has become an area of active research, with most studies centering on app analysis through release notes in the Apple App Store and tracking user reviews via issue trackers. However, the correlation between these release notes and user reviews in App Store remains understudied. Objective: In this paper, we introduce ROSEMATCHER, a novel automatic approach to match relevant user reviews with app release notes, and identify matched pairs with high confidence. Methods: We collected 944 release notes and 1,046,862 user reviews from 5 mobile apps in the Apple App Store as research data to evaluate the effectiveness and accuracy of ROSEMATCHER, and conducted deep content analysis on matched pairs. Results: Our evaluation shows that ROSEMATCHER can reach a hit ratio of 0.718 for identifying relevant matched pairs, and with the manual labeling and content analysis of 984 relevant pairs, we identify 8 roles that user reviews play in app updates according to the relationship between release notes and user reviews in the relevant matched pairs. Conclusions: Our findings indicate that both app development teams and users pay close attention to release notes and user reviews, with release notes typically addressing feature requests, bug reports, and complaints, and user reviews offering positive, negative, and constructive feedback. Overall, the study highlights the importance of the communication between app development teams and users in the release planning of mobile apps, with relevant reviews tending to be posed within a short period before and after the release of release notes, with the average time interval between the post time of release notes and user reviews being approximately one year.

Original languageEnglish
Article number107261
Number of pages17
JournalInformation and software technology
Volume161
Early online date19 May 2023
DOIs
Publication statusPublished - Sept 2023

Keywords

  • User reviews
  • Release notes
  • App store
  • Natural language processing
  • 2023 OA procedure

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