Automated Reporting of GUI Design Violations for Mobile Apps
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Abstract (arXiv)
The inception of a mobile app often takes form of a mock-up of the Graphical User Interface (GUI), represented as a static image delineating the proper layout and style of GUI widgets that satisfy requirements. Following this initial mock-up, the design artifacts are then handed off to developers whose goal is to accurately implement these GUIs and the desired functionality in code. Given the sizable abstraction gap between mock-ups and code, developers often introduce mistakes related to the GUI that can negatively impact an app's success in highly competitive marketplaces. Moreover, such mistakes are common in the evolutionary context of rapidly changing apps. This leads to the time-consuming and laborious task of design teams verifying that each screen of an app was implemented according to intended design specifications. This paper introduces a novel, automated approach for verifying whether the GUI of a mobile app was implemented according to its intended design. Our approach resolves GUI-related information from both implemented apps and mock-ups and uses computer vision techniques to identify common errors in the implementations of mobile GUIs. We implemented this approach for Android in a tool called GVT and carried out both a controlled empirical evaluation with open-source apps as well as an industrial evaluation with designers and developers from Huawei. The results show that GVT solves an important, difficult, and highly practical problem with remarkable efficiency and accuracy and is both useful and scalable from the point of view of industrial designers and developers. The tool is currently used by over one-thousand industrial designers and developers at Huawei to improve the quality of their mobile apps.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2018.
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Citing papers per year: 2023: 4; 2024: 6; 2025: 2; 2026: 3.
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- 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototypingcited by 50
- 2024Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approachcited by 24
- 2025ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agentscited by 23
- 2024UIClip: A Data-driven Model for Assessing User Interface Designcited by 14
- 2023Boosting GUI Prototyping with Diffusion Modelscited by 10
- 2023AidUI: Toward Automated Recognition of Dark Patterns in User Interfacescited by 9
- 2025Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Developmentcited by 5
- 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
- 2023EGFE: End-to-end Grouping of Fragmented Elements in UI Designs with Multimodal Learningcited by 2
- 2024GUI Element Detection Using SOTA YOLO Deep Learning Modelscited by 2
- 2024MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIscited by 0
- 2026TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity7 Oct 2026
- 2026Multi-modal user interface control detection using cross-attention8 Apr 2026
- 2026DOne: Decoupling Structure and Rendering for High-Fidelity Design-to-Code Generation12 Mar 2026
- 2025Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development29 Sep 2025
- 2025ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents30 Jul 2025
- 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping5 Nov 2024
- 2024GUI Element Detection Using SOTA YOLO Deep Learning Models7 Aug 2024
- 2024Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach24 Jun 2024
- 2024GUing: A Mobile GUI Search Engine using a Vision-Language Model30 Apr 2024
- 2024UIClip: A Data-driven Model for Assessing User Interface Design18 Apr 2024
- 2024MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs20 Mar 2024
- 2023EGFE: End-to-end Grouping of Fragmented Elements in UI Designs with Multimodal Learning18 Sep 2023
- 2023Boosting GUI Prototyping with Diffusion Modelscited by 10
- 2023Boosting GUI Prototyping with Diffusion Models9 Jun 2023
- 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototypingcited by 50
- 2024Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approachcited by 24
- 2024UIClip: A Data-driven Model for Assessing User Interface Designcited by 14
- 2023AidUI: Toward Automated Recognition of Dark Patterns in User Interfacescited 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
- 2023EGFE: End-to-end Grouping of Fragmented Elements in UI Designs with Multimodal Learningcited by 2
- 2024GUI Element Detection Using SOTA YOLO Deep Learning Modelscited by 2
- 2024MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIscited by 0
- 2026Multi-modal user interface control detection using cross-attentioncited by 0
- 2026Multi-modal user interface control detection using cross-attention8 Apr 2026
- 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping5 Nov 2024
- 2024GUI Element Detection Using SOTA YOLO Deep Learning Models7 Aug 2024
- 2024Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach24 Jun 2024
- 2024GUing: A Mobile GUI Search Engine using a Vision-Language Model30 Apr 2024
- 2024UIClip: A Data-driven Model for Assessing User Interface Design18 Apr 2024
- 2024MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs20 Mar 2024
- 2023EGFE: End-to-end Grouping of Fragmented Elements in UI Designs with Multimodal Learning18 Sep 2023
- 2023AidUI: Toward Automated Recognition of Dark Patterns in User Interfaces12 Mar 2023
- 2023An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation3 Jan 2023