The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps
Open accessAbstract
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 a suitable hierarchical GUI structure from which a prototype application can be automatically assembled. We implemented this approach for Android in a system called ReDraw. Our evaluation illustrates that ReDraw achieves an average GUI-component classification accuracy of 91 percent and assembles prototype applications that closely mirror target mock-ups in terms of visual affinity while exhibiting reasonable code structure. Interviews with industrial practitioners illustrate ReDraw's potential to improve real development workflows.
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1×
“At a high level, this approach extracts image features using the Speeded-Up Robust Feature (SURF) detection algorithm [99], then uses K-means clustering to cluster similar features together, and utilizes an SVM trained on resulting feature clusters.”
MethodGoing Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
Reverse Engineering Mobile Application User Interfaces with REMAUI (T)
Nguyen, T. A., & Csallner, C. (2015) · 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2015.32 · Cited by 200 · cited 10×
“To provide a performance comparison to REDRAW, we selected the two most closely related approaches in related research literature, REMAUI [8] and pix2code [38] , to provide a comparative baseline.”
BackgroundMethodInfluentialAutomated reporting of GUI design violations for mobile apps
Moran, K., Li, B., Bernal-Cárdenas, C., Jelf, D., & Poshyvanyk, D. (2018) · Proceedings of the 40th International Conference on Software Engineering · doi:10.1145/3180155.3180246 · Cited by 101
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An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
Tufano, M., Watson, C., Bavota, G., Penta, M. D., White, M., & Poshyvanyk, D. (2019) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3340544 · Cited by 370
Self-citationA Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
Watson, C., Cooper, N., Palacio, D. N., Moran, K., & Poshyvanyk, D. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3485275 · Cited by 137
Self-citationSketch2code: Generating a website from a paper mockup
Robinson, A. (2019) · arXiv · doi:10.48550/arxiv.1905.13750 · Cited by 18
A 2030 Roadmap for Software Engineering
Pezzè, M., Abrahão, S., Penzenstadler, B., Poshyvanyk, D., Roychoudhury, A., & Yue, T. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3731559 · Cited by 13
Self-citationGUIPilot: A Consistency-Based Mobile GUI Testing Approach for Detecting Application-Specific Bugs
Liu, R., Teoh, X., Lin, Y., Chen, G., Ren, R., Poshyvanyk, D., & Dong, J. S. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3728909 · Cited by 12
Self-citation
Metadata
- Authors
- Moran, K., Bernal-Cardenas, C., Curcio, M., Bonett, R., & Poshyvanyk, D.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2020-02-01
- Volume
- 46
- Issue
- 2
- Pages
- 196-221
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- 2326-3881
- OpenAlex
- W2785557022
- Cited by
- 239
- References
- 7
- FWCI
- 25.31
- Type
- journal article
- Language
- en
- Open access
- green
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