Open Source Python Software Testing Tools for Linux

Python Software Testing Tools for Linux

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Browse free open source Python Software Testing Tools for Linux and projects below. Use the toggles on the left to filter open source Python Software Testing Tools for Linux by OS, license, language, programming language, and project status.

  • Red Hat Enterprise Linux on Microsoft Azure Icon
    Red Hat Enterprise Linux on Microsoft Azure

    Deploy Red Hat Enterprise Linux on Microsoft Azure for a secure, reliable, and scalable cloud environment, fully integrated with Microsoft services.

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    Learn More
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  • 1
    Buildbot

    Buildbot

    Python-based continuous integration testing framework

    Buildbot is an open-source framework for automating software build, test, and release processes. At its core, Buildbot is a job scheduling system: it queues jobs, executes the jobs when the required resources are available, and reports the results. Your Buildbot installation has one or more masters and a collection of workers. The masters monitor source-code repositories for changes, coordinate the activities of the workers, and report results to users and developers. Workers run on a variety of operating systems. You configure Buildbot by providing a Python configuration script to the master. This script can be very simple, configuring built-in components, but the full expressive power of Python is available. This allows dynamic generation of configuration, customized components, and anything else you can devise. The framework itself is implemented in Twisted Python, and compatible with all major operating systems.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2
    Deepchecks

    Deepchecks

    Test Suites for validating ML models & data

    Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran.
    Downloads: 0 This Week
    Last Update:
    See Project
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