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

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2022-11-04 DOI:https://dl.acm.org/doi/10.1145/3531065
Sidong Feng, Minmin Jiang, Tingting Zhou, Yankun Zhen, Chunyang Chen
{"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":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"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\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2022-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/https://dl.acm.org/doi/10.1145/3531065\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"94","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3531065","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

大约50%的开发资源用于用户界面(UI)开发任务[9]。开发图标占用了大量的开发资源,这可能是一项耗时的任务,因为开发人员不仅需要考虑有效的实现方法,还需要考虑易于理解的描述。在本文中,我们将介绍Auto-Icon+,这是一种从设计工件中为图标自动生成可读且高效代码的方法。根据我们对设计师(图标由多个组件组合而成)和开发者(图标作为单个图像)之间的差异的采访,我们应用启发式聚类算法将组件组合成一个图标图像。然后,我们提出了一种基于深度学习模型和计算机视觉方法的方法,将合成的图标图像转换为带有描述性标签的字体,从而减少了开发人员费力的手工工作,促进了UI开发。我们在真实的UI开发环境中定量评估了我们的方法的质量,并证明我们的方法为开发人员提供了准确、高效、可读和可用的图标设计代码,节省了65.2%的实现时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊最新文献
Hyperbaric oxygen treatment promotes tendon-bone interface healing in a rabbit model of rotator cuff tears. Oxygen-ozone therapy for myocardial ischemic stroke and cardiovascular disorders. Comparative study on the anti-inflammatory and protective effects of different oxygen therapy regimens on lipopolysaccharide-induced acute lung injury in mice. Heme oxygenase/carbon monoxide system and development of the heart. Hyperbaric oxygen for moderate-to-severe traumatic brain injury: outcomes 5-8 years after injury.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1