A comparative study of Flutter and React Native for AI enhanced user feedback
Tanveer, MD Iftekhar Hossain (2025)
2025
Master's Programme in Computing Sciences and Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Hyväksymispäivämäärä
2025-12-19
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-2025121011433
https://urn.fi/URN:NBN:fi:tuni-2025121011433
Tiivistelmä
In recent years, the rapid growth of mobile application development has increased the de mand for high-quality software delivered efficiently. Cross-platform frameworks such as Flutter and React Native have emerged as popular solutions, enabling developers to build applications for both Android and iOS from a single codebase. This approach addresses the challenges associated with native development, which requires separate tools, lan guages, and environments for each platform.
This thesis evaluates Flutter and React Native across multiple dimensions, including per formance, development speed, UI consistency, developer experience, adaptive user inter faces, and the integration of AI-enhanced features such as sentiment analysis. A struc tured set of criteria was used to compare the frameworks and assess their suitability for modern mobile application development.
The study demonstrates that both frameworks are effective for creating high-quality cross-platform applications, with React Native showing particularly strong results in overall ef ficiency and flexibility. By providing a comprehensive comparison of these frameworks, this research offers practical guidance for developers and organizations aiming to inte grate AI-powered functionalities into mobile applications, helping them make informed decisions when selecting a cross-platform development framework
This thesis evaluates Flutter and React Native across multiple dimensions, including per formance, development speed, UI consistency, developer experience, adaptive user inter faces, and the integration of AI-enhanced features such as sentiment analysis. A struc tured set of criteria was used to compare the frameworks and assess their suitability for modern mobile application development.
The study demonstrates that both frameworks are effective for creating high-quality cross-platform applications, with React Native showing particularly strong results in overall ef ficiency and flexibility. By providing a comprehensive comparison of these frameworks, this research offers practical guidance for developers and organizations aiming to inte grate AI-powered functionalities into mobile applications, helping them make informed decisions when selecting a cross-platform development framework