Showing 34 open source projects for "framework-arduinoststm32"

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  • 1
    JMeter is a load testing framework developed in Java. This plugin adds functionality to JMeter that logs results to a database, allowing for more effective reporting.
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  • 2

    JDistUnit

    Java5 distributed JUnit with mobile agents.

    JDistUnit is a test framework for distributed unit testing with Java (think "JUnit on multiple computers at once"), useful for load and stress testing. Features hot deployment of test cases to the remote test servers.
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  • 3
    A Javascript framework designed for Web Applicaions.
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  • 4
    Shadowtail is a Java/Spring framework for gathering numerical data (statistics) about a Java application. It is particularly useful as a source of data to feed Orca, the free tool for plotting textual data onto a directory on a Web server.
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  • 5
    FTesT - Framework to tests
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  • 6
    Base C/C++ library providing unit testing framework with integrated memory leak and buffer overrun detection.
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  • 7

    AutoBlocks

    AutoBlocks is an automated testing framework.

    ...The framework is still in the works and needs things like telemetry and appropriate input/output data structures for the tests.
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  • 8

    PHP pUnit

    A small testing framework for PHP.

    A small testing framework for PHP based off some of .net's nUnit contracts. It uses a static assertion class rather than protects/public methods on an extended class.
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  • 9

    Alchemist plugin

    Alchemist GCC/LLVM plugin for code analysis and tuning

    News: since 2015 we continue all related developments within Collective Knowledge Framework: http://github.com/ctuning/ck/wiki Alchemist plugin is a collection of plugins for GCC/LLVM for external and fine-grain code analysis and tuning. It is intended to to extract program properties for machine learning based optimization (see MILEPOST GCC); optimize programs at fine-grain level (such as unrolling, tiling, prefetching, etc); tune default optimization heuristic; gradually decompose program and detect performance or other anomalies; generate benchmarks particularly useful to train ML-based compilers. ...
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