Many apps have basic accessibility issues, like missing labels or low contrast. Automated tools can help app developers catch basic issues, but can be laborious or require writing dedicated tests. We propose a system, motivated by a collaborative process with accessibility stakeholders at a large technology company, to generate whole app accessibility reports by combining varied data collection methods (e.g., app crawling, manual recording) with an existing accessibility scanner. Many such scanners are based on single-screen scanning, and a key problem in whole app accessibility reporting is to effectively de-duplicate and summarize issues collected across an app. To this end, we developed a screen grouping model with 96.9% accuracy (88.8% F1-score) and UI element matching heuristics with 97% accuracy (98.2% F1-score). We combine these technologies in a system to report and summarize unique issues across an app, and enable a unique pixel-based ignore feature to help engineers and testers better manage reported issues across their app's lifetime. We conducted a qualitative evaluation with 18 accessibility-focused engineers and testers which showed this system can enhance their existing accessibility testing toolkit and address key limitations in current accessibility scanning tools.

翻译：许多移动应用存在基本的无障碍问题，如缺少标签或对比度不足。自动化工具可帮助开发者发现基本问题，但操作繁琐或需编写专用测试。我们提出一种与大型科技公司无障碍利益相关方的协作流程驱动的系统，通过结合多样化数据采集方法（如应用爬取、手动录制）与现有无障碍扫描器，生成完整的应用无障碍报告。现有扫描器多基于单屏扫描，而全应用无障碍报告的核心难题在于高效去重并汇总应用内收集到的问题。为此，我们开发了准确率96.9%（F1分数88.8%）的屏幕分组模型，以及准确率97%（F1分数98.2%）的UI元素匹配启发式算法。我们将这些技术整合到系统中，用于报告和汇总应用内的唯一问题，并引入基于像素的忽略功能，帮助工程师和测试人员更好地管理应用生命周期内的报告问题。我们与18名专注于无障碍的工程师和测试人员进行了定性评估，结果表明该系统能增强现有无障碍测试工具包，并解决当前无障碍扫描工具的关键局限性。