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Accessibility Evaluation of SHIEN

Evaluating a website for accessibility.


Coursework for INST631
Fundamentals of HCI


 Accessibility Evaluator


Individual Project


WAVE, JAWS, WCAG 2.1 Guidelines, Color Contrast Checker


2 Weeks

About the Project

The Exercise

This project was an exercise in accessibility evaluation of my selected website - SHEIN. The evaluation was done in two stages - first was a manual evaluation using the WCAG 2.1 guidelines, which I verified using JAWS, color picker and other independent tools. The second part involved the use of WAVE Tool to automatically detect the flaws and which guideline they violate.

The two stages are described below:

Part 1 - Manual Accessibility Evaluation

Shein is an online retailer that specializes in the fast fashion segment. I am a regular
user of the website because it offers the best deals on clothes, especially if you are on a
budget. It was also named as the ‘Most Popular Brand in the World’ in 2022 by

First, I went through each point in the WCAG 2.1 guidelines to look for potential violations, which I verified using JAWS Screen Reader, Color Picker and Contrast Checker, etc . I used the version JAWS 2023 on my browser to first run through the website using a screen reader, traversing through it using the various shortcuts available. Where I suspeced a violation, I used the 'Inspect Element' option to look for missing alt texts, headings, etc.

The report can be found here.

Part 2 - Automatic Usability Evaluation

This task involved the use of WAVE Accessibility Evaluation tool to detect accessibility issues within the website. I had to use the skills from Part 1 to cross-check the results from WAVE, and report on errors, and false positive and negatives. 

The report can be found here.


This assignment acquainted me with not just the tools available for assessing accessibility of
webpages, but the flaws that the webpages that I use regularly have. It is very easy to overlook
flaws in a website if one is not empathetic enough and if we leave some gaps in a manual
evaluation. On the flip side, it is important to manually evaluate too, as the automatic tools
may not take into account the edge cases.

I found my findings using WAVE to be more detailed than the ones I manually detected. For
example, I couldn’t pick up a certain contrast violation because the text looked normal to me,
but it was detected by WAVE.
The WAVE evaluation, however, gave a few warnings that turned out to be false. For example,
it detected certain alt text entries to be redundant, while they were needed for
The screen reader was able to describe images even without the Alt text because of the ARIA
elements present, which WAVE detected, but it still flagged the lack of alt text.
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