Understanding the landscape of software modelling assistants for MDSE tools: A systematic mapping

IF 3.8 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Information and Software Technology Pub Date : 2024-05-21 DOI:10.1016/j.infsof.2024.107492

David Mosquera , Marcela Ruiz , Oscar Pastor , Jürgen Spielberger

{"title":"Understanding the landscape of software modelling assistants for MDSE tools: A systematic mapping","authors":"David Mosquera , Marcela Ruiz , Oscar Pastor , Jürgen Spielberger","doi":"10.1016/j.infsof.2024.107492","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>Model Driven Software Engineering (MDSE) and low-code/no-code software development tools promise to increase quality and productivity by modelling instead of coding software. One of the major advantages of modelling software is the increased possibility of involving diverse stakeholders since it removes the barrier of being IT experts to actively participate in software production processes. From an academic and industry point of view, the main question remains: What has been proposed to assist humans in software modelling tasks?</p></div><div><h3>Objective</h3><p>In this paper, we systematically elucidate the state of the art in assistants for software modelling and their use in MDSE and low-code/no-code tools.</p></div><div><h3>Method</h3><p>We conducted a systematic mapping to review the state of the art and answer the following research questions: i) how is software modelling assisted? ii) what goals and limitations do existing modelling assistance proposals report? iii) which evaluation metrics and target users do existing modelling assistance proposals consider? For this purpose, we selected 58 proposals from 3.176 screened records and reviewed 17 MDSE and low-code/no-code tools from main market players published by the Gartner Magic Quadrant.</p></div><div><h3>Result</h3><p>We clustered existing proposals regarding their modelling assistance strategies, goals, limitations, evaluation metrics, and target users, both in research and practice.</p></div><div><h3>Conclusions</h3><p>We found that both academic and industry proposals recognise the value of assisting software modelling. However, documentation about MDSE assistants’ limitations, evaluation metrics, and target users is scarce or non-existent. With the advent of artificial intelligence, we expect more assistants for MDSE and low-code/no-code software development will emerge, making imperative the need for well-founded frameworks for designing modelling assistants focused on addressing target users’ needs and advancing the state of the art.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"173 ","pages":"Article 107492"},"PeriodicalIF":3.8000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924000971/pdfft?md5=6da25ebb0cb2c28f672df388b54839e1&pid=1-s2.0-S0950584924000971-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Software Technology","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950584924000971","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Context

Model Driven Software Engineering (MDSE) and low-code/no-code software development tools promise to increase quality and productivity by modelling instead of coding software. One of the major advantages of modelling software is the increased possibility of involving diverse stakeholders since it removes the barrier of being IT experts to actively participate in software production processes. From an academic and industry point of view, the main question remains: What has been proposed to assist humans in software modelling tasks?

Objective

In this paper, we systematically elucidate the state of the art in assistants for software modelling and their use in MDSE and low-code/no-code tools.

Method

We conducted a systematic mapping to review the state of the art and answer the following research questions: i) how is software modelling assisted? ii) what goals and limitations do existing modelling assistance proposals report? iii) which evaluation metrics and target users do existing modelling assistance proposals consider? For this purpose, we selected 58 proposals from 3.176 screened records and reviewed 17 MDSE and low-code/no-code tools from main market players published by the Gartner Magic Quadrant.

Result

We clustered existing proposals regarding their modelling assistance strategies, goals, limitations, evaluation metrics, and target users, both in research and practice.

Conclusions

We found that both academic and industry proposals recognise the value of assisting software modelling. However, documentation about MDSE assistants’ limitations, evaluation metrics, and target users is scarce or non-existent. With the advent of artificial intelligence, we expect more assistants for MDSE and low-code/no-code software development will emerge, making imperative the need for well-founded frameworks for designing modelling assistants focused on addressing target users’ needs and advancing the state of the art.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

了解用于 MDSE 工具的软件建模辅助工具的情况：系统制图

背景建模驱动软件工程（MDSE）和低代码/无代码软件开发工具承诺通过建模而不是编码软件来提高质量和生产率。建模软件的主要优势之一是增加了不同利益相关者参与的可能性，因为它消除了信息技术专家积极参与软件生产过程的障碍。从学术界和工业界的角度来看，主要问题依然存在：在本文中，我们系统地阐明了软件建模辅助工具的发展现状，以及它们在 MDSE 和低代码/无代码工具中的应用。为此，我们从 3.176 条筛选记录中选取了 58 项提案，并查阅了 Gartner Magic Quadrant 公布的主要市场参与者的 17 种 MDSE 和低代码/无代码工具。结果我们对现有提案进行了分类，涉及其建模辅助策略、目标、局限性、评估指标和目标用户，包括研究和实践两方面。然而，有关 MDSE 辅助工具的局限性、评估指标和目标用户的文献却很少或根本不存在。随着人工智能时代的到来，我们预计将会出现更多用于 MDSE 和低代码/无代码软件开发的辅助工具，这使得设计建模辅助工具的框架必须有充分的依据，以满足目标用户的需求并推进技术发展。

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

Information and Software Technology 工程技术-计算机：软件工程

CiteScore

9.10

自引率

7.70%

发文量

164

审稿时长

9.6 weeks

期刊介绍： Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include: • Software management, quality and metrics, • Software processes, • Software architecture, modelling, specification, design and programming • Functional and non-functional software requirements • Software testing and verification & validation • Empirical studies of all aspects of engineering and managing software development Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information. The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.