CLC number: TP391.3
On-line Access: 2015-06-04
Received: 2014-11-02
Revision Accepted: 2015-04-21
Crosschecked: 2015-05-18
Cited: 3
Clicked: 7200
Citations: Bibtex RefMan EndNote GB/T7714
Meng-ni Zhang, Can Wang, Jia-jun Bu, Zhi Yu, Yu Zhou, Chun Chen. A sampling method based on URL clustering for fast web accessibility evaluation[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 449-456.
@article{title="A sampling method based on URL clustering for fast web accessibility evaluation",
author="Meng-ni Zhang, Can Wang, Jia-jun Bu, Zhi Yu, Yu Zhou, Chun Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="6",
pages="449-456",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1400377"
}
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%A Yu Zhou
%A Chun Chen
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400377
TY - JOUR
T1 - A sampling method based on URL clustering for fast web accessibility evaluation
A1 - Meng-ni Zhang
A1 - Can Wang
A1 - Jia-jun Bu
A1 - Zhi Yu
A1 - Yu Zhou
A1 - Chun Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 6
SP - 449
EP - 456
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1400377
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.
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