Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps
Kevin Moran, Carlos Bernal-C\'ardenas, Michael Curcio and, Richard Bonett, Denys Poshyvanyk

TL;DR
This paper introduces ReDraw, an automated system that uses computer vision, neural networks, and data-driven algorithms to rapidly prototype mobile app GUIs from mock-ups, reducing manual effort and improving accuracy.
Contribution
ReDraw is the first integrated system combining detection, classification, and assembly for GUI prototyping, achieving high accuracy and practical utility for Android app development.
Findings
GUI component classification accuracy of 91%
Prototypes closely match mock-ups visually
Potential to streamline mobile app development workflows
Abstract
It is common practice for developers of user-facing software to transform a mock-up of a graphical user interface (GUI) into code. This process takes place both at an application's inception and in an evolutionary context as GUI changes keep pace with evolving features. Unfortunately, this practice is challenging and time-consuming. In this paper, we present an approach that automates this process by enabling accurate prototyping of GUIs via three tasks: detection, classification, and assembly. First, logical components of a GUI are detected from a mock-up artifact using either computer vision techniques or mock-up metadata. Then, software repository mining, automated dynamic analysis, and deep convolutional neural networks are utilized to accurately classify GUI-components into domain-specific types (e.g., toggle-button). Finally, a data-driven, K-nearest-neighbors algorithm generates…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
