benchmarking web accessibility evaluation tools: measuring the harm of sole reliance on automated...

Benchmarking Web Accessibility Evaluation Tools:

10th International Cross-Disciplinary Conference on Web AccessibilityW4A2013

Markel Vigo University of Manchester (UK)Justin Brown Edith Cowan University (Australia) Vivienne Conway Edith Cowan University (Australia)

Measuring the Harm of Sole Reliance on Automated Tests

http://dx.doi.org/10.6084/m9.figshare.701216

Problem & Fact

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WWW is not accessible

Evidence

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Webmasters are familiar with accessibility guidelines

Lazar et al., 2004Improving web accessibility: a study of webmaster perceptions

Computers in Human Behavior 20(2), 269–288

Hypothesis I

Assuming guidelines do a good job...

H1: Accessibility guidelines awareness is not that widely spread.

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Evidence II

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Webmasters put compliance logos on non-compliant websites

Gilbertson and Machin, 2012Guidelines, icons and marketable skills: an accessibility evaluation of 100 web development company homepages

W4A 2012

Hypothesis II

Assuming webmasters are not trying to cheat...

H2: A lack of awareness on the negative effects of overreliance on automated tools.

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• It's easy

• In some scenarios seems like the only option: web observatories, real-time...

• We don't know how harmful they can be

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Expanding on H2Why we rely on automated tests

• If we are able to measure these limitations we can raise awareness

• Inform developers and researchers

• We run a study with 6 tools

• Compute coverage, completeness and correctness wrt WCAG 2.0

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Expanding on H2Knowing the limitations of tools

• Coverage: whether a given Success Criteria (SC) is reported at least once

• Completeness:

• Correctness:

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MethodComputed Metrics

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Vision Australiawww.visionaustralia.org.au

• Non-profit• Non-government• Accessibility resource

Prime Ministerwww.pm.gov.au

• Federal Government• Should abide by the Transition Strategy

Transperthwww.transperth.wa.gov.au

• Government affiliated• Used by people with disabilities

MethodStimuli

MethodObtaining the "Ground Truth"

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Ad-hoc sampling

Manual evaluation

Agreement

Ground truth

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Evaluate Compare with the GT

MethodComputing Metrics

Computemetrics

T1

For every page in the sample...

T2

T3

T4

T5

T6

R1

R2

R3

R4

R5

R6

Get reports

GT

M1

M2

M3

M4

M5

M6

Accessibility of Stimuli

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Vision Australiawww.visionaustralia.org.au

Prime Ministerwww.pm.gov.au

Transperthwww.transperth.wa.gov.au

• 650 WCAG Success Criteria violations (A and AA)

• 23-50% of SC are covered by automated test

• Coverage varies across guidelines and tools

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ResultsCoverage

• Completeness ranges in 14-38%

• Variable across tools and principles

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ResultsCompleteness per tool

• How conformance levels influence on completeness

• Wilcoxon Signed Rank: W=21, p<0.05

• Completeness levels are higher for 'A level' SC

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ResultsCompleteness per type of SC

• How accessibility levels influence on completeness

• ANOVA: F(2,10)=19.82, p<0.001

• The less accessible a page is the higher levels of completeness

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ResultsCompleteness vs. accessibility

• Cronbach's α = 0.96

• Multidimensional Scaling (MDS)

• Tools behave similarly

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ResultsTool Similarity on Completeness

• Tools with lower completeness scores exhibit higher levels of correctness 93-96%

• Tools that obtain higher completeness yield lower correctness 66-71%

• Tools with higher completeness are also the most incorrect ones

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ResultsCorrectness

• We corroborate that 50% is the upper limit for automatising guidelines

• Natural Language Processing?– Language: 3.1.2 Language of parts– Domain: 3.3.4 Error prevention

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ImplicationsCoverage

• Automated tests do a better job...

...on non-accessible sites

...on 'A level' success criteria

• Automated tests aim at catching stereotypical errors

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ImplicationsCompleteness I

• Strengths of tools can be identified across WCAG principles and SC

• A method to inform decision making

• Maximising completeness in our sample of pages– On all tools: 55% (+17 percentage points)– On non-commercial tools: 52%

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ImplicationsCompleteness II

Conclusions

• Coverage: 23-50%

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• Completeness: 14-38%

• Higher completeness leads to lower correctness

Follow up

13 May 2013 24

Contact@markelvigo | markel.vigo@manchester.ac.uk

Presentation DOIhttp://dx.doi.org/10.6084/m9.figshare.701216

Datasetshttp://www.markelvigo.info/ds/bench12/index.html

10th International Cross-Disciplinary Conference on Web AccessibilityW4A2013