Abstract
The typical task assigned to a developer for any design created by a designer is very crucial when it comes to building any software. The process involves tedious and repetitive steps of adapting to rapidly changing client requirements and thus, accommodating those changes in the prototype. This poses a barrier to the software development process. As a solution to this problem, we introduce a model that can be used to automate the process of generating front-end code from hand-drawn wireframes by leveraging deep learning techniques. The image features of the GUI mock-up which is fed to the model as input are extracted to generate markup tags as tokens and rendered in the form of reusable components achieving an overall BLEU score of 0.92.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
O.S. Ramón, J.G. Molina, J.S. Cuadrado, J. Vanderdonckt, GUI Generation from Wireframes
T. Beltramelli, Pix2code: generating code from a graphical user interface screenshot. Preprint arXiv:1705.07962 (2017), p. 9
M. Balog, DeepCoder: learning to write programs, in International Conference on Learning Representations (2017)
A.L. Gaun, M. Brockschmidt, R. Singh, N. Kushman, P. Kohli, J. Taylor, D. Tarlow, TerpreT: A Probabilistic Programming Language for Program Induction, in arXiv (2016)
Airbnb. [Online]. Available https://airbnb.design/sketching-interfaces (2018)
“Sacha, the A.I. web designer,” Firedrop.ai. [Online]. Available https://sacha.ai/
“Wix ADI,” Wix, [Online]. Available https://support.wix.com/en/article/creating-an-adi-site
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in ICLR (2015)
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation (1997), p. 32
F.A. Gers, J. Schmidhuber, F. Cummins, Learning to forget: continual prediction with LSTM, in Proceedings of ICANN’99 International Conference on Artificial Neural Networks (London, 1999)
A. Graves, Generating sequences with recurrent neural networks. Preprint arxiv:1308.0850 (2013)
A.M. Schafer, S. Udluft, H.G. Zimmermann, Learning long term dependencies with recurrent neural networks, in International Conference on Artificial Neural Networks (2006)
T. Tieleman, G. Hinton, Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude, in COURSERA: Neural Networks for Machine Learning (2012)
W. Zaremba, I. Sutskever, O. Vinyals, Recurrent Neural Network Regularization (2014)
A. Karpathy, L. Fei-Fei, Deep visual-semantic alignments for generating image descriptions (2015)
K. Potdar, T.S. Pardawala, C.D. Pai, A comparative study of categorical variable encoding techniques for neural network classifiers. Int. J. Comput. Appl. 175(4), 7–9 (2017)
I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde- Farley, S. Ozair, A. Courville and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems, 2014
K. Papineni, S. Roukos, T. Ward, W.J. Zhu, BLEU: a method for automatic evaluation of machine translation, in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL) (2002)
C.C. Tuan Anh Nguyen, Reverse Engineering Mobile Application
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, P., Chaudhary, V., Malhotra, N., Gupta, N., Mittal, M. (2020). Dynamic Web with Automatic Code Generation Using Deep Learning. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_61
Download citation
DOI: https://doi.org/10.1007/978-981-15-1286-5_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1285-8
Online ISBN: 978-981-15-1286-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)