Showing 4 open source projects for "stress"

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  • 1
    loadable components

    loadable components

    The recommended code splitting library for React

    Loadable Components is an MIT-licensed open source project. It's an independent project with ongoing development. React code splitting made easy. Reduce your bundle size without stress. A React code splitting library, not an alternative to React.lazy. A solution recommended by React Team. Library splitting, prefetching, server side rendering, and full dynamic import features.
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  • 2
    Big List of Naughty Strings

    Big List of Naughty Strings

    List of strings which have a high probability of causing issues

    The Big List of Naughty Strings is a community-maintained catalog of “gotcha” inputs that commonly break software, from unusual Unicode to SQL and script injection payloads. It exists so developers and QA engineers can easily test edge cases that normal test data would miss, such as zero-width characters, right-to-left marks, emojis, foreign alphabets, and long or malformed strings. By throwing these strings at forms, APIs, databases, and UIs, teams can discover encoding bugs, sanitizer...
    Downloads: 0 This Week
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  • 3

    Faker

    A PHP library that generates fake data for you

    ...It can also populate entities using an ORM or an ODM, and features language-specific formatters as well. If you need to bootstrap your database, fill-in your persistence for stress testing, or anonymize data taken from a production service, Faker is a great choice for you.
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  • 4
    benchm-ml

    benchm-ml

    A benchmark of commonly used open source implementations

    ...The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories.
    Downloads: 0 This Week
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