2025/04/27 by Mohammadali Mohammadkhani, Mohammadkhani, Mohammadali, Sara Movahed +7
Computer Science · Social Sciences · #Digital Accessibility for Disabilities #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2504.19085
openalex publication_date 2025/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Low-code applications are gaining popularity across various fields, enabling non-developers to participate in the software development process. However, due to the strong reliance on graphical user interfaces, they may unintentionally exclude users with visual impairments, such as color blindness and low vision. This paper investigates the accessibility issues users report when using low-code applications. We construct a comprehensive dataset of low-code application reviews, consisting of accessibility-related reviews and non-accessibility-related reviews. We then design and implement a complex model to identify whether a review contains an accessibility-related issue, combining two state-of-the-art Transformers-based models and a traditional keyword-based system. Our proposed hybrid model achieves an accuracy and F1-score of 78% in detecting accessibility-related issues.