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...Conda quickly installs, runs, and updates packages and their dependencies. Conda easily creates, saves, loads, and switches between environments on your local computer. It was created for Python programs but it can package and distribute software for any language. Conda as a package manager helps you find and install packages. If you need a package that requires a different version of Python, you do not need to switch to a different environment manager because conda is also an environment manager. With just a few commands, you can set up a totally separate environment to run that different version of Python, while continuing to run your usual version of Python in your normal environment.

Scrapy is a fast, open source, high-level framework for crawling websites and extracting structured data from these websites. Portable and written in Python, it can run on Windows, Linux, macOS and BSD. Scrapy is powerful, fast and simple, and also easily extensible. Simply write the rules to extract the data, and add new functionality if you wish without having to touch the core. Scrapy does the rest, and can be used in a number of applications. It can be used for data mining, monitoring and automated testing.

...It is built on top of popular docker solutions and includes Postgres database, Zebrunner Reporting, Jenkins Master/Slaves Nodes, Selenium Hub, Mobile Device Farm (MCloud), SonarQube, etc. All components are deployed under NGINX WebServer which can be configured in a fully secured environment. The Zebrunner CE repository: https://github.com/zebrunner/zebrunner

The Software Testing Automation Framework (STAF) is a framework designed to improve the level of reuse and automation in test cases and test environments. The goal of STAF is to provide a complete end-to-end automation solution for testers.

GNU/Linux Desktop Testing Project (GNU/LDTP) is created with the goal of producing high quality test automation frameworks and cutting-edge tools that can be used to test the Linux Desktop and improve it.

Testerman is an attempt to produce a TTCN-3 inspired test automation system without the strict typing model of the TTCN-3 test control notation that can be too heavy to be practical with non-stricly defined protocols. This is achieved by bringing TTCN-3 primitives and concepts to the Python programming language. It provides a complete environment to design, manage, execute and analyze automated tests, and can be used as a platform to develop test simulators (drivers and stubs) and prototypes of network applications.