TY - GEN
T1 - Automated Cross-Platform GUI Code Generation for Mobile Apps
AU - Chen, Sen
AU - Fan, Lingling
AU - Su, Ting
AU - Ma, Lei
AU - Liu, Yang
AU - Xu, Lihua
N1 - Funding Information:
We appreciate the reviewers' constructive feedback. This work is partially supported by NSFC Grant 61502170, NTU Research Grant NGF-2017-03-033 and NRF Grant CRDCG2017-S04.
Funding Information:
ACKNOWLEDGMENTS We appreciate the reviewers’ constructive feedback. This work is partially supported by NSFC Grant 61502170, NTU Research Grant NGF-2017-03-033 and NRF Grant CRDCG2017-S04.
PY - 2019/3/21
Y1 - 2019/3/21
N2 - Android and iOS are the two dominant platforms for building mobile apps. To provide uniform and smooth user experience, app companies typically employ two teams of programmers to develop UIs (and underlying functionalities) for these two platforms, respectively. However, this development practice is costly for both development and maintenance. To reduce the cost, we take the first step in this direction by proposing an automated cross-platform GUI code generation framework. It can transfer the GUI code implementation between the two mobile platforms. Specifically, our framework takes as input the UI pages and outputs the GUI code for the target platform (e.g., Android or iOS). It contains three phases, i.e., component identification, component type mapping, and GUI code generation. It leverages image processing and deep learning classification techniques. Apart from the UI pages of an app, this framework does not require any other inputs, which makes it possible for large-scale, platform -independent code generation.
AB - Android and iOS are the two dominant platforms for building mobile apps. To provide uniform and smooth user experience, app companies typically employ two teams of programmers to develop UIs (and underlying functionalities) for these two platforms, respectively. However, this development practice is costly for both development and maintenance. To reduce the cost, we take the first step in this direction by proposing an automated cross-platform GUI code generation framework. It can transfer the GUI code implementation between the two mobile platforms. Specifically, our framework takes as input the UI pages and outputs the GUI code for the target platform (e.g., Android or iOS). It contains three phases, i.e., component identification, component type mapping, and GUI code generation. It leverages image processing and deep learning classification techniques. Apart from the UI pages of an app, this framework does not require any other inputs, which makes it possible for large-scale, platform -independent code generation.
UR - http://www.scopus.com/inward/record.url?scp=85064208602&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064208602&partnerID=8YFLogxK
U2 - 10.1109/AI4Mobile.2019.8672718
DO - 10.1109/AI4Mobile.2019.8672718
M3 - Conference contribution
AN - SCOPUS:85064208602
T3 - AI4Mobile 2019 - 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile
SP - 13
EP - 16
BT - AI4Mobile 2019 - 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile
A2 - Liu, Yang
A2 - Xue, Minhui
A2 - Ma, Lei
A2 - Li, Li
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Workshop on Artificial Intelligence for Mobile, AI4Mobile 2019
Y2 - 24 February 2019
ER -