Investigating Developer Experiences with UI Components in Flutter: Challenges and Implications
The rapid proliferation of smartphones, particularly those using the Android operating system, has positioned Android as the leading platform in the mobile market. With the advent of various mobile app development frameworks such as Ionic, Flutter, Corona SDK, and Xamarin, developers face the challe...
| 總結: | The rapid proliferation of smartphones, particularly those using the Android operating system, has positioned Android as the leading platform in the mobile market. With the advent of various mobile app development frameworks such as Ionic, Flutter, Corona SDK, and Xamarin, developers face the challenge of selecting the optimal framework for their projects. This study focuses on the Flutter framework, a cross-platform development tool by Google, exploring developers' experiences with User interface (UI) construction, specifically in handling common components such as network images and animations. A comprehensive survey was conducted, targeting Flutter developers from diverse backgrounds, including engineers, university students, and instructors. The survey aimed to identify the challenges associated with using Flutter's NetworkImage feature and Animation component. Results indicate significant issues with performance, network connectivity, image quality, dependence on third-party libraries, cache management, and traffic consumption for network images. Similarly, animations in Flutter pose challenges related to memory insufficiency, varying effects across different devices, and performance impacts on user experience. The findings highlight the necessity for enhanced support and innovative solutions within the Flutter framework to address these limitations. This study serves as a resource for developers to better understand and navigate the complexities of Flutter, and it underscores the importance of further research to improve mobile application development technologies. Future investigations should broaden the scope to include more diverse application cases and larger sample sizes to build on these initial insights. © 2024 IEEE. |
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| DOI: | 10.1109/IVIT62102.2024.10692626 |