SHF: Small: Detecting and Repairing Accessibility Failures in Web Applications

SHF：小：检测和修复 Web 应用程序中的辅助功能故障

• 批准号: 2009045
• 负责人: William Halfond
• 金额: $ 49.58万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2009045&HistoricalAwards=false
• 关键词: SHF; Small; Detecting; Repairing; Accessibility

Companies create web applications to reach a broad audience and enable customers to access services and information. For many Americans, particularly the 26% with some type of disability, access to web applications is particularly important and can represent a critical lifeline for them, providing access to resources that would otherwise be inaccessible. However, a recent study found that 70% of internet sites have “accessibility blockers” that make critical functionality inaccessible to disabled users. These kinds of accessibility problems negatively affect the ability of disabled web users to successfully navigate a web application’s user interface and can cause a web site to become almost completely unusable to users who are physically unable to use the standard point-and-click mouse-based interfaces. This project will lead to the development of techniques for automatically finding these kinds of accessibility problems in web applications. These techniques will enable software developers to efficiently and accurately search through large web applications to find potential accessibility problems and provide the developers with useful information that will make the process of repairing the problems faster and more accurate.The project will encompass four technical research thrusts. The first thrust will define a key abstraction, the Keyboard Navigation Flow Graph (KNFG), which will represent the ways a keyboard-based user can navigate a web application, and develop automated techniques that can analyze a web page and automatically extract the KNFG. The second thrust will design graph-based algorithms for analyzing the KNFG that, with high accuracy, can detect a range of accessibility problems present in a web page. The third thrust will lead to the development of localization techniques that can identify the specific HTML or JavaScript elements in a web page that are responsible for the accessibility problems. The fourth and final thrust will explore the design of techniques for automatically repairing accessibility problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

公司创建 Web 应用程序是为了覆盖广泛的受众，并使客户能够访问服务和信息，对于许多美国人来说，尤其是 26% 患有某种残疾的人，访问 Web 应用程序尤其重要，可以为他们提供访问的关键生命线。然而，最近的一项研究发现，70% 的互联网网站都有“辅助功能拦截器”，使残障用户无法访问关键功能，这些辅助功能问题会对残障网络用户成功导航的能力产生负面影响。 Web 应用程序的用户界面，并且可能导致网站对于那些身体上无法使用标准的基于点击鼠标的界面的用户来说几乎完全无法使用。该项目将导致自动发现此类可访问性问题的技术的开发。这些技术将使软件开发人员能够高效、准确地搜索大型 Web 应用程序，以发现潜在的可访问性问题，并为开发人员提供有用的信息，使修复问题的过程更快、更准确。该项目将包含四项技术。第一个主旨将定义一个关键的抽象，键盘导航流图 (KNFG)，它将代表基于键盘的用户导航 Web 应用程序的方式，并开发可以分析网页并自动提取 KNFG 的自动化技术。第二个重点将设计基于图的算法。用于分析 KNFG，它可以高精度地检测网页中存在的一系列可访问性问题。第三个推动力将导致本地化技术的发展，该技术可以识别网页中负责的特定 HTML 或 JavaScript 元素。对于可访问性问题。第四个也是最后一个重点将探索自动修复可访问性问题的技术设计。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。