Accessibility of low-code approaches: A systematic literature review

Model-driven approaches are increasingly used in different domains, such as education, finance and app development, in order to involve non-developers in the software development process. Such tools are hugely dependent on visual elements and thus might not be accessible for users with specific chal...

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Bibliographic Details
Published in:Information and software technology Vol. 177; p. 107570
Main Authors: Khalajzadeh, Hourieh, Grundy, John
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2025
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ISSN:0950-5849
Online Access:Get full text
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Summary:Model-driven approaches are increasingly used in different domains, such as education, finance and app development, in order to involve non-developers in the software development process. Such tools are hugely dependent on visual elements and thus might not be accessible for users with specific challenges, e.g., visual impairments. To locate and analyse existing literature on the accessibility of low-code approaches, their strengths and weaknesses and key directions for future research. We carried out a systematic literature review and searched through five leading databases for primary studies. We used both quantitative and qualitative methods for data synthesis. After reviewing and filtering 918 located studies, and conducting both backward and forward snowballing, we identified 38 primary studies that were included in our analysis. We found most papers focusing on accessibility of visual languages and block-based programming. Limited work has been done on improving low code programming environment accessibility. The findings of this systematic literature review will assist researchers and developers in understanding the accessibility issues in low-code approaches and what has been done so far to develop accessible approaches. •We report an analysis of the literature on the accessibility of low-code approaches.•We investigate the accessibility considerations in the existing literature.•We categorise the model-driven methods adopted in the existing literature.•We inform other researchers of the future directions in making low-code approaches accessible.
ISSN:0950-5849
DOI:10.1016/j.infsof.2024.107570