Monitoring Temporal Dynamics of Issues in Crowdsourced User Reviews and their Impact on Mobile App Updates

Analyzing user feedback from app stores through opinion mining aims to support software engineering activities, specifically in software maintenance and evolution. It is essential to promptly detect emerging app issues and facilitate the software's ongoing development. Manual analysis is impractical due to the large volume of textual data, necessitating machine learning methods for automation. Current methods lack mechanisms for trend detection and monitoring temporal dynamics, considering the relationship between issues and app release dates. This paper presents a two-fold approach: (i) identifying app issues and (ii) monitoring their evolution through temporal dynamic modeling using time series, release dates, and alerts. We present the MApp-TIME (Monitoring App by Temporal dynamic of Issues for app Maintenance and Evolution) approach, a microservices architecture designed to detect and monitor the temporal dynamics of issues and app releases. The goal is to reduce the time between issue detection and resolution, facilitating better software maintenance and evolution. We analyzed 13 million reviews across 20 domains and the findings revealed that about 75% of app releases correspond with issue peaks in the analyzed time series. Monitoring the temporal dynamics of crowdsourced user reviews can allow us to detect and prioritize issues early, sianificantly mitigating their impact.