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

公司创建Web应用程序以覆盖广泛的受众，并使客户能够访问服务和信息。对于许多美国人来说，特别是26%的残疾人，访问Web应用程序尤为重要，可以代表他们的关键生命线，提供对资源的访问，否则将无法访问。然而，最近的一项研究发现，70%的互联网网站都有“可访问性阻止程序”，使残疾用户无法访问关键功能。 这些类型的可访问性问题负面地影响了残疾的web用户成功地浏览web应用程序的用户界面的能力，并且可能导致web站点对于身体上无法使用基于鼠标的标准点击式界面的用户变得几乎完全不可用。 该项目将导致开发自动发现Web应用程序中这些类型的可访问性问题的技术。 这些技术将使软件开发人员能够有效和准确地搜索大型网络应用程序，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。该项目将包括四个技术研究重点。第一个重点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可以导航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。第二个目标是设计基于图形的算法来分析KNFG，该算法具有高精度，可以检测网页中存在的一系列可访问性问题。第三个重点将导致本地化技术的发展，这些技术可以识别网页中导致可访问性问题的特定HTML或JavaScript元素。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.