StoryDroid: Automated Generation of Storyboard for Android Apps
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Abstract (arXiv)
Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The review process is crucial in the sense that it reduces market risks and provides inspiration for app development. However, manual exploration of hundreds of existing apps by different roles (e.g., product manager, UI/UX designer, developer) in a development team can be ineffective. For example, it is difficult to completely explore all the functionalities of the app in a short period of time. Inspired by the conception of storyboard in movie production, we propose a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, StoryDroid extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Our comprehensive experiments unveil that StoryDroid is effective and indeed useful to assist app development. The outputs of StoryDroid enable several potential applications, such as the recommendation of UI design and layout code.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Feb 2019.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
8 papers.
- 2021GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networkscited by 17
- 2021GLIB: Towards Automated Test Oracle for Graphically-Rich Applicationscited by 9
- 2023An Empirical Investigation into the Use of Image Captioning for Automated Software Documentationcited by 4
- 2024GUing: A Mobile GUI Search Engine using a Vision-Language Modelcited by 4
- 2022ULDGNN: A Fragmented UI Layer Detector Based on Graph Neural Networkscited by 2
- 2022Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executablescited by 2
- 2022UI Layers Merger: Merging UI layers via Visual Learning and Boundary Priorcited by 1
- 2026MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigationcited by 0
- 2026MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigation20 Mar 2026
- 2024GUing: A Mobile GUI Search Engine using a Vision-Language Model30 Apr 2024
- 2023An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation3 Jan 2023
- 2022Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables4 Sep 2022
- 2022ULDGNN: A Fragmented UI Layer Detector Based on Graph Neural Networks13 Aug 2022
- 2022UI Layers Merger: Merging UI layers via Visual Learning and Boundary Prior18 Jun 2022
- 2021GLIB: Towards Automated Test Oracle for Graphically-Rich Applications19 Jun 2021
- 2021GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks25 Jan 2021