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.

Analyzing mobile app reviews is essential for identifying trends and issue patterns that affect user experience and app reputation in app stores. A risk matrix provides a straightforward, intuitive method to prioritize software maintenance actions to mitigate negative ratings. However, manually constructing a risk matrix is time-consuming, and stakeholders often struggle to understand the context of risks due to varied descriptions and the sheer volume of reviews. Therefore, machine learning-based methods are needed to extract risks and classify their priority effectively. While existing studies have automated risk matrix generation in software development, they have not explored app reviews or utilized Large Language Models (LLMs) in a scalable architecture. To address this gap, we present iRisk (scalable microservice for classifying issue Risks), a tool for generating a risk matrix based on crowdsourced app reviews using LLM. We present i-LLAMA, a fine-tuned version of LLaMA 3, optimized to detect and prioritize app-related issues using a risk analysis dataset of reviews categorized by severity and likelihood of occurrence. This dataset is also publicly available. Our contributions include the open-source resources to support the software maintenance and evolution industry, fine-tuning of LLaMA 3, and a scalable microservice architecture to handle large volumes of data. The iRisk can manage app issues and risks and provide an automated dashboard and visualizations for decision-making, monitoring, and risk mitigation. The tool is available on GitHub11https://github.com/vitormesaque/iRisk, and a presentation about the tool can be found in this video22https://irisk.mappidea.com.