Assessing the Effort of Repairing the Accessibility of Web Sites
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Assessing the Effort of Repairing the Accessibility of Web Sites

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Assessing the Effort of Repairing the Accessibility of Web Sites Assessing the Effort of Repairing the Accessibility of Web Sites Presentation Transcript

  • ASSESSING THE EFFORT OF REPAIRING THEACCESSIBILITY OF WEB SITES Nádia Fernandes, Luís Carriço University of Lisbon ICCHP, Linz, Austria, July 11-13, 2012
  • ICCHP, Linz, Austria, July 13, 2012 2
  • ICCHP, Linz, Austria, July 13, 2012 3Introduction• 40-50% of the Web content uses templates.• Evaluations are performed in pages as a whole.• Obfuscating results.• Available metrics may be misleading.
  • ICCHP, Linz, Austria, July 13, 2012 4Objectives1. Assess the effort of repairing a site’s accessibility that was originally developed using templates Requirements: • Develop a new metric, • Template detection algorithm2. Introducing accessible templates as a form of rapidly repairing a page
  • ICCHP, Linz, Austria, July 13, 2012 5The Accessibility Repairing Effort Metric (AREM) The metric considers the sum of the number of fails and warnings reported by accessibility evaluation techniques, excluding repeated instances.“Primitive elements”- elements of a page, when an element thatis part of a template is only considered once.
  • ICCHP, Linz, Austria, July 13, 2012 6The Accessibility Repairing Effort Metric (AREM)The purpose of this metric is:1. assess the quality measurement of the accessibility of site construction (effort person.month);2. and not the perceived quality of the site towards end-users.
  • ICCHP, Linz, Austria, July 13, 2012 7The Platform for Accessibility EvaluationBasis: • QualWeb evaluator • Fast Match algorithm
  • ICCHP, Linz, Austria, July 13, 2012 8Procedure1. The DOM trees are obtained2. The pages are compared using the Fast Match algorithm • Comparative function • Result: The “primitive” nodes3. The accessibility evaluation is executed (QualWeb evaluator)4. Reports generation (new reports)5. The repairing effort estimation according to AREM is computed.
  • ICCHP, Linz, Austria, July 13, 2012 9Experimental Study• Objective: understand the advantage of using AREM and validate the template detection method.• Object of study: 15 sites with templates (Alexa Top 100) 5 high 5 low level level 5 medium level
  • ICCHP, Linz, Austria, July 13, 2012 10Experimental Study• QualWeb was applied with the template aware option set.• The values for the AREM using the element primitiveness.• We used a conservative rate metric to obtain values of accessibility quality to compare with AREM. rate conservative =
  • ICCHP, Linz, Austria, July 13, 2012 11Metrics Results – AREM (I) 100000 80000 60000 40000 20000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Web sites Non-primitive Primitive 1 – Higher level of template usage, 15 – Lower level of template usage
  • ICCHP, Linz, Austria, July 13, 2012 12Metrics Results - Conservative rate (I) 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Web sites Non-primitive Primitive 1 – Higher level of template usage, 15 – Lower level of template usage
  • ICCHP, Linz, Austria, July 13, 2012 13Metrics Results – AREM (II) 100000 80000 60000 40000 20000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Web sites Non-primitive Primitive 1 – Higher level of template usage, 15 – Lower level of template usage
  • ICCHP, Linz, Austria, July 13, 2012 14Metrics Results - Conservative rate (II) 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Web sites Non-primitive Primitive 1 – Higher level of template usage, 15 – Lower level of template usage
  • ICCHP, Linz, Austria, July 13, 2012 15Metrics Results - Conservative rate (III) 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Web sites Non-primitive Primitive 1 – Higher level of template usage, 15 – Lower level of template usage
  • ICCHP, Linz, Austria, July 13, 2012 16Validating the Approach % errors on template Web sites % template detectionHigher level of 56% 14%template detection 51% 8%Mid level of template 33% 6%detection 31% 12%Lower level of 18% 7%template detection 15% 1% The number of incorrect elements detected by Fast Match algorithm is less than 10% (average).
  • ICCHP, Linz, Austria, July 13, 2012 17Discussion• The validation of the template detection yielded a deviation.• The algorithm had more failures in sites which use more template based components.• Regarding the AREM metric: • The difference between the computed values is high; • Less 30% of repairing issues (primitive elements); • Depending on the sites this value can decrease substantially.• These results confirm and support our previous experiment’s results.
  • ICCHP, Linz, Austria, July 13, 2012 18Conclusion• Templates can be very important, reducing the effort of correction.• The metric defined is a real indicator of the work that have to be done, unlike certain quality metric that can be misleading.• We performed a validation experiment of both metric and framework and conclude that the template detection algorithm has a high efficacy.
  • ICCHP, Linz, Austria, July 13, 2012 19Future Work1. Improvements of Fast-Match algorithm to guarantee a higher accuracy level;2. A large-scale evaluation of the fast match algorithm.
  • ICCHP, Linz, Austria, July 13, 2012 20Thank younadiaf@di.fc.ul.pt