Browsing by Author "Zhou, Changkong"
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- Challenges and solutions in cross-platform mobile development: a qualitative study of Flutter and React Native
Perustieteiden korkeakoulu | Master's thesis(2024-05-20) Zhou, ChangkongThis thesis explores the challenges and solutions in cross-platform mobile application development, focusing on Flutter and React Native frameworks. By engaging 20 developers in semi-structured interviews, this research rigorously examines their firsthand experiences with these technologies. The study identifies main challenges such as performance optimization, UI consistency, and integrating with native functionalities. It also examines the effectiveness of the solutions, including data caching and state management tools, to overcome these obstacles and how such challenges influence the overall developer experience (DX). The findings reveal that despite the efficiency gains provided by cross-platform frameworks, developers encounter complex obstacles, including learning curve of new languages, bridging communication with native modules, and adapting to frequent version updates. The study contributes practical insights for Flutter and React Native usage in mobile app development, with an emphasis on improving DX to address the evolving needs of the mobile app development. - Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces
A4 Artikkeli konferenssijulkaisussa(2024-05-11) Jiang, Yue; Zhou, Changkong; Garg, Vikas; Oulasvirta, AnttiPresent-day graphical user interfaces (GUIs) exhibit diverse arrangements of text, graphics, and interactive elements such as buttons and menus, but representations of GUIs have not kept up. They do not encapsulate both semantic and visuo-spatial relationships among elements. To seize machine learning’s potential for GUIs more efficiently, Graph4GUI exploits graph neural networks to capture individual elements’ properties and their semantic—visuo-spatial constraints in a layout. The learned representation demonstrated its effectiveness in multiple tasks, especially generating designs in a challenging GUI autocompletion task, which involved predicting the positions of remaining unplaced elements in a partially completed GUI. The new model’s suggestions showed alignment and visual appeal superior to the baseline method and received higher subjective ratings for preference. Furthermore, we demonstrate the practical benefits and efficiency advantages designers perceive when utilizing our model as an autocompletion plug-in.