Showing 4 open source projects for "source code plagiarism detection"

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  • 1
    Highlight.js

    Highlight.js

    JavaScript syntax highlighter with language auto-detection

    Highlight.js is a syntax highlighter written in JavaScript. It works in the browser as well as on the server. It can work with pretty much any markup, doesn’t depend on any other frameworks, and has automatic language detection. Highlight.js supports over 180 languages in the core library. There are also 3rd party language definitions available to support even more languages. We strongly recommend <pre><code> wrapping for code blocks. It's quite semantic and "just works" out of the box with...
    Downloads: 5 This Week
    Last Update:
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  • 2
    ThinkReview

    ThinkReview

    AI-powered code reviews for GitLab & Azure DevOps. Zero setup. Powered

    I Copilot for Gitlab and Azure DevOps Merge requests (MRs) and Pull Requests (PRs) in your browser. The extension Summarises merge requests , provided suggestions and find potential security issues , provide smart questions to dive deeper and chat with your pull requests The extension works out of the box without any complex setup - Supports custom Self hosted Gitlab as well as gitlab.com and gitlab enterprise Enhance your GitLab MRs and Azure Devops PRsworkflows with automatic...
    Downloads: 0 This Week
    Last Update:
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  • 3

    phpMD / PHP CodeSniffer Netbeans Plugin

    Netbeans Plugin for phpmd, phpcs, checkstyle and phpcpd.

    Netbeans PHP Plugin which provides violations detected by PHP Mess Detector (phpmd.org by Manuel Pichler) and warnings / infos from PHP CodeSniffer (http://pear.php.net/package/PHP_CodeSniffer/) also displayes dublicated php code detected by PHP Copy & Paste Detector (phpcpd by Sebastian Bergmann https://github.com/sebastianbergmann/phpcpd) and even supports PHP Checkstyle (http://code.google.com/p/phpcheckstyle/) Plugin by http://www.fin-sn.de
    Downloads: 3 This Week
    Last Update:
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  • 4
    Medical Datasets (In a text file, with space separated values) can be loaded to the system. By choosing either one of the two classifiers, Neural network or Decision Tree, the system can be trained and evaluated.
    Downloads: 0 This Week
    Last Update:
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