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@@ -14,6 +14,26 @@ A11YBench consists of 60 real-world web projects, encompassing 147 web pages and
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  The projects vary substantially in size, from 123 to 43,198 source files and from 3,610 to 1,555,532 lines of code, covering both lightweight documentation sites and large production-grade applications.
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  This scale ensures that A11YBench reflects the structural and technical diversity of modern web ecosystems.
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  ## 🚀Dataset Features
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  ### Dataset Split
 
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  The projects vary substantially in size, from 123 to 43,198 source files and from 3,610 to 1,555,532 lines of code, covering both lightweight documentation sites and large production-grade applications.
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  This scale ensures that A11YBench reflects the structural and technical diversity of modern web ecosystems.
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+ ## 🔍Dataset Collection
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+
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+ We consider real, actively maintained GitHub repositories implementing production websites using JavaScript or TypeScript, including frameworks such as React, Next.js, Vue, and static site generators.
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+ Using GitHub's search with keyword `website` and language filters `JavaScript`, `TypeScript`, we sort by `Best Match` and collect the top 500 repositories for screening.
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+ We select repositories showing active maintenance by keeping those with over 100 total pull requests, 10 stars, and at least one commit within the past month.
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+ After manual verification of setup documentation and reproducibility.
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+
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+ To ensure reproducibility, we keep only projects that can be successfully built and executed in a local environment.
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+ Following prior benchmark practices (e.g., [SWE-bench](https://huggingface.co/datasets/SWE-bench/SWE-bench_Multimodal), we manually follow installation guides, resolve dependencies, and verify successful local deployment.
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+ Projects lacking clear setup instructions or exhibiting build or runtime errors are discarded.
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+ For each successfully deployed project, we manually inspect the homepage and linked subpages to ensure correct rendering and navigation.
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+ Here, we randomly pick 60 deployable and visually functional web projects as our test set.
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+ Next, we identify reproducible test instances suitable for accessibility evaluation.
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+ For each project, we select the homepage and several randomly sampled subpages (linked from the homepage),
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+ and manually remove any inappropriate pages (such as plain text elements, project documentation, etc).
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+ Following [prior studies](https://github.com/seal-hub/GenA11y) that highlight the IBM Accessibility Checker as one of the most reliable open-source web accessibility checker tools, we use it to detect accessibility violations and verify consistent results across multiple runs.
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+ Pages that trigger checker errors or yield inconsistent outputs are excluded.
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+ After filtering, 60 projects with 145 stable and reproducible test pages remain for accessibility repair evaluation.
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+
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  ## 🚀Dataset Features
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  ### Dataset Split