@inproceedings{d5d10294c0b24c7ea34eebe541a2c1d5,
title = "Code Implementation Recommendation for Android GUI Components",
abstract = "We present a prototype tool Icon2Code, targeted to helping app de-velopers 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 imple-menting 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 https://youtu.be/pM3ZBGrQTdQ.",
keywords = "API Recommendation, Android, App Development, Collaborative Filtering, Icon Imple-mentation",
author = "Yanjie Zhao and Li Li and Xiaoyu Sun and Pei Liu and John Grundy",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 44th ACM/IEEE International Conference on Software Engineering: Companion proceedings, ICSE-Companion 2022 ; Conference date: 22-05-2022 Through 27-05-2022",
year = "2022",
month = oct,
day = "19",
doi = "10.1109/ICSE-Companion55297.2022.9793746",
language = "英语",
series = "Proceedings - International Conference on Software Engineering",
publisher = "IEEE Computer Society",
pages = "31--35",
booktitle = "Proceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering",
address = "美国",
}