A systematic literature review on automatic website generation

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Journal of Computer Languages Pub Date : 2023-06-01 DOI:10.1016/j.cola.2023.101202
Thisaranie Kaluarachchi, Manjusri Wickramasinghe
{"title":"A systematic literature review on automatic website generation","authors":"Thisaranie Kaluarachchi,&nbsp;Manjusri Wickramasinghe","doi":"10.1016/j.cola.2023.101202","DOIUrl":null,"url":null,"abstract":"<div><p>Since machine learning became a prominent feature in the modern-day computing landscape, the urge to automate processes has increased. One such process of particular interest has been the automatic generation of websites based on user intention. Though the requirement of such automatic generation is a modern-day need, the quality of the automatic generation still provides a unique set of challenges. As such, to analyze these unique challenges and viable opportunities in automatic website generation, this survey systematically reviews research on the topics of automatic website generation. The analysis initially segments state-of-the-art into three categories based on the dominant strategy used for automatic generation. These strategies are examples-based, mock-up-driven, and artificial intelligence-driven automatic website generation. When considering the example-based strategy, the emphasis is on analyzing how manual design aspects of a professionally developed website are incorporated into generation models and the challenges that arise. Similarly, transformation methods from website visual design into functional GUI code are investigated for the mock-up-driven strategy with a particular reference to the six underlying conversion mechanisms. Finally, artificial intelligence website builders are analyzed based on their ability to build customizable websites to user preferences. Based on this systematic review of 47 research works on the three dominant strategies, this survey outlines unique challenges and future research endeavors that researchers would encounter when developing models that generate websites automatically and provides insights to researchers on selecting a website generation strategy based on user intention appropriately.</p></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"75 ","pages":"Article 101202"},"PeriodicalIF":1.7000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Languages","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590118423000126","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 4

Abstract

Since machine learning became a prominent feature in the modern-day computing landscape, the urge to automate processes has increased. One such process of particular interest has been the automatic generation of websites based on user intention. Though the requirement of such automatic generation is a modern-day need, the quality of the automatic generation still provides a unique set of challenges. As such, to analyze these unique challenges and viable opportunities in automatic website generation, this survey systematically reviews research on the topics of automatic website generation. The analysis initially segments state-of-the-art into three categories based on the dominant strategy used for automatic generation. These strategies are examples-based, mock-up-driven, and artificial intelligence-driven automatic website generation. When considering the example-based strategy, the emphasis is on analyzing how manual design aspects of a professionally developed website are incorporated into generation models and the challenges that arise. Similarly, transformation methods from website visual design into functional GUI code are investigated for the mock-up-driven strategy with a particular reference to the six underlying conversion mechanisms. Finally, artificial intelligence website builders are analyzed based on their ability to build customizable websites to user preferences. Based on this systematic review of 47 research works on the three dominant strategies, this survey outlines unique challenges and future research endeavors that researchers would encounter when developing models that generate websites automatically and provides insights to researchers on selecting a website generation strategy based on user intention appropriately.

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
关于网站自动生成的系统文献综述
自从机器学习成为现代计算领域的一个突出特征以来,自动化过程的呼声越来越高。一个特别感兴趣的过程是基于用户意图自动生成网站。尽管这种自动发电的需求是现代的需求,但自动发电的质量仍然提供了一系列独特的挑战。因此,为了分析网站自动生成中的这些独特挑战和可行机会,本调查系统地回顾了网站自动生成主题的研究。该分析最初根据用于自动生成的主要策略将最先进技术分为三类。这些策略是基于示例、模型驱动和人工智能驱动的自动网站生成。在考虑基于示例的策略时,重点是分析如何将专业开发的网站的手动设计方面纳入生成模型以及出现的挑战。类似地,针对实体模型驱动的策略,研究了从网站视觉设计到功能GUI代码的转换方法,并特别参考了六种底层转换机制。最后,分析了人工智能网站建设者根据用户偏好构建可定制网站的能力。基于对这三种主要策略的47项研究工作的系统回顾,本调查概述了研究人员在开发自动生成网站的模型时会遇到的独特挑战和未来的研究努力,并为研究人员提供了根据用户意愿适当选择网站生成策略的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Computer Languages
Journal of Computer Languages Computer Science-Computer Networks and Communications
CiteScore
5.00
自引率
13.60%
发文量
36
期刊最新文献
Debugging in the Domain-Specific Modeling Languages for multi-agent systems GPotion: Embedding GPU programming in Elixir Near-Pruned single assignment transformation of programs MLAPW: A framework to assess the impact of feature selection and sampling techniques on anti-pattern prediction using WSDL metrics Editorial Board
×
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