Code Implementation Recommendation for Android GUI Components
We present a prototype tool Icon2Code, targeted to helping app developers more quickly implement the callback functions of complex Android GUI components by recommending code implementations learnt from similar GUI components from other apps. Given an icon or UI widget provided by designers, Icon2Code first queries a large pre-established database to locate similar icons that other apps have utilized. It then leverages a collaborative filtering model to suggest the most relevant APIs and their usage examples associated with the intended behaviours of these icons. Experimental results on 5,000 randomly selected real-world apps show that Icon2Code is useful and effective in recommending code examples for implementing the behaviours of complex GUI components. It has over 50% of success rate when only one recommended API is taken into account, and over 94% of success rate if 20 APIs are considered. The video demo can be found at~\url{https://youtu.be/pM3ZBGrQTdQ}.
Wed 11 MayDisplayed time zone: Eastern Time (US & Canada) change
20:00 - 21:00 | Mining Software RepositoriesDEMO - Demonstrations at ICSE Demo room 1 Chair(s): Xiao Qu ABB Corporate Research | ||
20:00 15mDemonstration | ARSearch: Searching for API Related Resources from Stack Overflow and GitHub DEMO - Demonstrations Kien Luong School of Computing and Information Systems, Singapore Management University, Ferdian Thung Singapore Management University, David Lo Singapore Management University Media Attached | ||
20:15 15mDemonstration | gDefect4DL: A Dataset of General Real-World Deep Learning Program Defects DEMO - Demonstrations Yunkai Liang Tianjin University, Yun Lin National University of Singapore, Xuezhi Song Fudan University, Jun Sun Singapore Management University, Zhiyong Feng Tianjin University, Jin Song Dong National University of Singapore Pre-print Media Attached | ||
20:30 15mDemonstration | Code Implementation Recommendation for Android GUI Components DEMO - Demonstrations Yanjie Zhao Monash University, Li Li Monash University, Xiaoyu Sun Monash University, Pei Liu Monash University, John Grundy Monash University Pre-print Media Attached | ||