Improving the Efficiency of Mobile User Interface Development through Semantic and Data-Driven Analyses
Creators: name: Chen, Jieshan
Publisher: name: The Australian National University
Year: 2022

description: Having millions of mobile applications from Google Play and Apple's App store, the smartphone is becoming a necessity in our life. People could access a wide variety of services by using the mobile application, between which user interfaces (UIs) work as an important proxy.A well-designed UI makes an application easy, practical, and efficient to use. However, due to the rapid application iteration speed and the shortage of UI designers, developers are required to design the UIs and implement them in a short time.As a result, they may be unaware of or compromise some important factors related to usability and accessibility during the process of developing user interfaces of mobile applications.Therefore, efficient and useful tools are needed to enhance the efficiency of the development of user interfaces. In this thesis, I proposed three techniques to improve the efficiency of designing and developing user interfaces through semantic and data-driven analyses. First, I proposed a UI design search engine to help designers or developers quickly create trendy and practical UI designs by exposing them to UI designs in real applications. I collected a large-scale UI design dataset by automatically exploring UIs from top-downloaded Android applications, and designed an image autoencoder-based UI design engine to enable finer-grained UI design search. Second, during the process of understanding the real UIs implementation, I found that existing applications have a severe accessibility issue of lacking labels for image-based buttons. Such an issue will hinder the blind users to access the key functionalities on UIs. As blind users need to rely on screen readers to read content on UIs, it requires the developers to set up appropriate labels for image-based buttons.Therefore, I proposed LabelDroid, which aims to automatically generate labels (i.e., the content description) of image-based buttons while developers implement UIs. Finally, as the above techniques all require the view hierarchical information, which contains the bounds and type of contained elements, to achieve the goal, it is essential to generalize these techniques to a broader scope. For example, UIs in the design-sharing platforms do not have any metadata about the elements. To do this, I conducted the first large-scale empirical study on evaluating existing object detection methods of detecting elements in UIs. By understanding the unique characteristics of UI elements and UIs, I proposed a hybrid method to boost the accuracy and precision of detecting elements on user interfaces. Such a fundamental method can be beneficial to many downstream applications, such as UI design search, UI code generation, and UI testing. In conclusion, I proposed three techniques to enhance the efficiency of designing and developing the user interfaces on mobile applications through semantic and data-driven analyses. Such methods could easily generalize to a broader scope, such as user interfaces of desktop apps and websites.I expect my proposed techniques and the understanding of user interfaces can facilitate the following research.