David Mosquera , Marcela Ruiz , Oscar Pastor , Jürgen Spielberger
{"title":"了解用于 MDSE 工具的软件建模辅助工具的情况：系统制图","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":"{\"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}","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}
Understanding the landscape of software modelling assistants for MDSE tools: A systematic mapping
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.
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.
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