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https://github.com/karma-riuk/crab-webapp.git
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made a better about modal content
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@ -47,7 +47,7 @@ header .info-btn {
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padding: 0;
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}
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fieldset .info-btn {
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fieldset .info-btn, .modal-overlay .info-btn{
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padding: revert;
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}
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@ -58,7 +58,7 @@
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<br /><br />
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<div style="display: flex; align-items: center; gap: 0.5em">
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<button id="upload-btn">Upload JSON</button>
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<div id="upload-status" class="hidden" style="color: green;"> hello world </div>
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<div id="upload-status" class="hidden" style="color: green;"></div>
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</div>
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</fieldset>
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@ -127,20 +127,45 @@
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<template id="about">
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<h2>About this project</h2>
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<div>
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<p>CRAB (Code Review Automation Benchmark) is a research-driven platform designed to evaluate deep
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learning models for code review tasks. Developed as part of a master's thesis at the Università
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della Svizzera italiana, CRAB provides a high-quality, curated benchmark dataset of Java code review
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triplets: submitted code, reviewer comment, and revised code. Each instance is manually validated to
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ensure that reviewer comments directly address code issues and that the revised code implements the
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feedback accurately. </p>
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<p>The platform supports two core tasks: generating human-like review comments and refining code based
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on those comments. It also accounts for paraphrased feedback and alternative valid code revisions,
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offering a more realistic and robust evaluation. CRAB addresses the shortcomings of existing
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datasets by eliminating noise and ensuring functional correctness through testing. Researchers can
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upload model predictions to receive standardized evaluations, making CRAB an essential tool for
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advancing automated code review technologies.</p>
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<p>
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This project introduces <strong>CRAB (Code Review Automated Benchmark)</strong>, a high-quality
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benchmark designed to evaluate deep learning-based code review automation tools. It focuses on two
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key tasks:
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</p>
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<ul>
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<li><strong>Comment Generation</strong>: Generating natural language review comments that identify
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issues and suggest improvements for a given piece of code.</li>
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<li><strong>Code Refinement</strong>: Producing revised code that correctly implements the
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suggestions from a review comment.</li>
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</ul>
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<p>
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The dataset consists of
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carefully curated triplets
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<code><submitted_code, reviewer_comment, revised_code></code>—ensuring each comment is
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actionable and each revision implements the suggested change. This eliminates noise common in
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previous datasets and supports reliable, meaningful evaluation.
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</p>
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<p>
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To support model benchmarking, we also provide a web-based evaluation platform (the website on which
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you are reading this description) that allows researchers to download the dataset, submit their
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predictions, and assess model performance across both tasks.
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</p>
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<p>
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You can explore the source code for each component here:
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</p>
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<ul>
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<li><a href="https://github.com/karma-riuk/crab" target="_blank">Dataset Construction Repository</a>
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</li>
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<li><a href="https://github.com/karma-riuk/crab-webapp" target="_blank">Web App Repository</a></li>
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</ul>
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<p>
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This website lets you evaluate code review models against the CRAB benchmark. You can download input
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files for either the comment generation or code refinement task, upload your model’s predictions,
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and view the results once processing is complete. Each section includes a help icon
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<button class='info-btn'><i class="fa fa-info"></i></button> that provides more detailed
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instructions and file format guidelines.
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</p>
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</div>
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</template>
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