{"title":"Auto-Icon+:一个自动的端到端代码生成工具,用于UI开发中的图标设计","authors":"Sidong Feng, Minmin Jiang, Tingting Zhou, Yankun Zhen, Chunyang Chen","doi":"https://dl.acm.org/doi/10.1145/3531065","DOIUrl":null,"url":null,"abstract":"<p>Approximately 50% of development resources are devoted to user interface (UI) development tasks [9]. Occupying a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this article, we present <monospace>Auto-Icon+</monospace>, an approach for automatically generating readable and efficient code for icons from design artifacts. According to our interviews to understand the gap between designers (icons are assembled from multiple components) and developers (icons as single images), we apply a heuristic clustering algorithm to compose the components into an icon image. We then propose an approach based on a deep learning model and computer vision methods to convert the composed icon image to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real-world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon designs, in terms of saving 65.2% implementing time.</p>","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"316 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Auto-Icon+: An Automated End-to-End Code Generation Tool for Icon Designs in UI Development\",\"authors\":\"Sidong Feng, Minmin Jiang, Tingting Zhou, Yankun Zhen, Chunyang Chen\",\"doi\":\"https://dl.acm.org/doi/10.1145/3531065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Approximately 50% of development resources are devoted to user interface (UI) development tasks [9]. Occupying a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this article, we present <monospace>Auto-Icon+</monospace>, an approach for automatically generating readable and efficient code for icons from design artifacts. According to our interviews to understand the gap between designers (icons are assembled from multiple components) and developers (icons as single images), we apply a heuristic clustering algorithm to compose the components into an icon image. We then propose an approach based on a deep learning model and computer vision methods to convert the composed icon image to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real-world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon designs, in terms of saving 65.2% implementing time.</p>\",\"PeriodicalId\":48574,\"journal\":{\"name\":\"ACM Transactions on Interactive Intelligent Systems\",\"volume\":\"316 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2022-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Interactive Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/https://dl.acm.org/doi/10.1145/3531065\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Interactive Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3531065","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Auto-Icon+: An Automated End-to-End Code Generation Tool for Icon Designs in UI Development
Approximately 50% of development resources are devoted to user interface (UI) development tasks [9]. Occupying a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this article, we present Auto-Icon+, an approach for automatically generating readable and efficient code for icons from design artifacts. According to our interviews to understand the gap between designers (icons are assembled from multiple components) and developers (icons as single images), we apply a heuristic clustering algorithm to compose the components into an icon image. We then propose an approach based on a deep learning model and computer vision methods to convert the composed icon image to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real-world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon designs, in terms of saving 65.2% implementing time.
期刊介绍:
The ACM Transactions on Interactive Intelligent Systems (TiiS) publishes papers on research concerning the design, realization, or evaluation of interactive systems that incorporate some form of machine intelligence. TIIS articles come from a wide range of research areas and communities. An article can take any of several complementary views of interactive intelligent systems, focusing on:
the intelligent technology,
the interaction of users with the system, or
both aspects at once.