迈向包容性低代码开发：检测用户评论中的可访问性问题
Toward Inclusive Low-Code Development: Detecting Accessibility Issues in User Reviews
AI总结：
本文针对低代码应用可能排斥视觉障碍用户的问题，构建评论数据集并提出结合两个Transformer模型与关键词系统的混合模型，以78%的准确率和F1分数检测可访问性问题。
摘要：
低代码应用在各领域日益普及，使非开发者能够参与软件开发过程。然而，由于对图形用户界面的强烈依赖，它们可能无意中排斥有视觉障碍的用户，如色盲和低视力用户。本文研究了用户在使用低代码应用时报告的可访问性问题。我们构建了一个全面的低代码应用评论数据集，包含可访问性相关评论和非可访问性相关评论。随后，我们设计并实现了一个复杂模型来识别评论是否包含可访问性问题，该模型结合了两个基于Transformer的最先进模型和一个传统的基于关键词的系统。我们提出的混合模型在检测可访问性问题方面达到了78%的准确率和F1分数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
