Uppsats
Performance and feature support of Progressive Web Applications : A performance and available feature comparison between Progressive Web Applications, React Native applications and native iOS applications.
Master-uppsats
KTH/Skolan för elektroteknik och datavetenskap (EECS)
Publicerad: 2022
Språk: Engelska
Sammanfattning
Mobile platform fragmentation is one of the main challenges of mobile development today, forcing developers to develop one application for each targeted platform, which significantly impacts time and cost for application development and maintenance. The fragmentation has given rise to cross-platform application development tools and frameworks, making it possible to develop one single application compatible with several platforms. This thesis focuses on the web-based approach Progressive Web Applications (PWAs), which, in contrast to previous approaches, targets both mobile and desktop devices. We aim to point out the supported features, evaluate their suitability for QR code scanning, and their performance compared to alternative approaches on iOS. We specifically cover a feature set of 33 features and measure response times, CPU and memory utilization, geolocation accuracy, and QR code scanning correctness. We developed three benchmark artifacts for the performance analysis: a PWA, a React Native application, and a native iOS application, and conducted automated run-time experiments using the tools Xcode and XCUITest. The performance evaluation shows that native applications performed best in memory and CPU utilization, whereas React Native achieved the shortest response times. The feature evaluation shows that the majority of the features are supported or partially supported for PWAs, and that the support continues to grow. Still, PWAs lack support for crucial mobile features such as push notifications and background synchronization, making PWAs insufficient for advanced mobile application development on iOS. Nevertheless, this study shows that PWAs are well worth considering for applications with low requirements.
Information
- Författare
- Nilsson, Anders
- Lärosäte / institution
- KTH/Skolan för elektroteknik och datavetenskap (EECS)
- Publiceringsdatum
- 2022
- Uppsatstyp
- Master-uppsats
- Språk
- Engelska
Utforska vidare
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