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Abstract: When evaluating the accessibility of a large website, we rely on sampling methods to reduce the cost of evaluation. This may lead to a biased evaluation when the distribution of checkpoint violations in a website is skewed and the selected samples do not provide a good representation of the entire website. To improve sampling quality, stratified sampling methods first cluster web pages in a site and then draw samples from each cluster. In existing stratified sampling methods, however, all the pages in a website need to be analyzed for clustering, causing huge I/O and computation costs. To address this issue, we propose a novel page sampling method based on URL clustering for web accessibility evaluation, namely URLSamp. Using only the URL information for stratified page sampling, URLSamp can efficiently scale to large websites. Meanwhile, by exploiting similarities in URL patterns, URLSamp cluster pages by their generating scripts and can thus effectively detect accessibility problems from web page templates. We use a data set of 45 web sites to validate our method. Experimental results show that our URLSamp method is both effective and efficient for web accessibility evaluation.

The paper is very interesting, and the theme is relevant. The authors address problems that developers have to face day by day. The proposed approach is simple and quite good.

基于URL聚类的快速无障碍检测抽样方法

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