pynguin
falcon
pynguin | falcon | |
---|---|---|
11 | 9 | |
1,200 | 9,388 | |
1.0% | 0.2% | |
8.2 | 7.1 | |
11 days ago | 17 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pynguin
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There is framework for everything.
https://swagger.io/specification/ https://github.com/se2p/pynguin
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Supposed to create tests for a massive project, how should I go about it?
I would use black to reformat this, then, if you can't refactor/rewrite (which is a lot of work!) I would try automated test generation via something like pynguin or fuzzing. I mean … this is not going to be a reliable solution anyways if the codebase is like that. So I would go in a direction that I find interesting to learn about and that could be helpful for the project. That would be generating tests and doing fuzzing. In the end you should run some linters anyways so that you can justify your results and show that the task is not in the scope of an internship and needs extensive refactoring.
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Klara: Python automatic test generations and static analysis library
The main difference that Klara bring to the table, compared to similar tool like pynguin and Crosshair is that the analysis is entirely static, meaning that no user code will be executed, and you can easily extend the test generation strategy via plugin loading (e.g. the options arg to the Component object returned from function above is not needed for test coverage).
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Does anybody know a simple algorithm for generating unit tests given a function's code?
Automated White-box test generation software: * https://github.com/EMResearch/EvoMaster -- for integration tests. * https://github.com/se2p/pynguin, https://pynguin.readthedocs.io/en/latest/user/quickstart.html -- unit test generation for python
- se2p/pynguin Pynguin, the PYthoN General UnIt test geNerator, is a tool that allows developers to generate unit tests automatically.
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Hacker News top posts: Jun 1, 2021
Pynguin – Generate Python unit tests automatically\ (60 comments)
- Pynguin – Generate Python unit tests automatically
- Pynguin – Allow developers to generate Python unit tests automatically
falcon
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Is something wrong with FastAPI?
Falcon FastAPI Sanic Starlite (disclosure: I do work here)
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A Look on Python Web Performance at the end of 2022
Sanic is very very popular with 16.6k stars, 1.5k forks, opencollective sponsors and a very active github. Falcon is more popular than japronto with 8.9k stars, 898 forks, opencollective sponsors and a very active github too. Despite Japronto been keeped as first place by TechEmPower, Falcon is a way better solution in general with performance similar to fastify an very fast node.js framework that hits 575k requests per second in this benchmark.
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Flask vs FastAPI?
I prefer Falcon for kicking up an API.
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Python for everyone : Mastering Python The Right Way
Falcon
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Pyjion – A Python JIT Compiler
And here's a project that's mostly Python, and optionally uses Cython https://github.com/falconry/falcon
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2 Questions to Ask Before Choosing a Python Framework
To help with the above two cases I would consider using a microframework, and the Python community provides many solutions. In my professional career I’ve had the opportunity to work with three very good alternatives to Django: Flask, Falcon, and Fast API. Flask is designed to be easy to use and extend. It follows the principles of minimalism and gives more control over the app. Choosing it, developers can use multiple types of databases, which is not easy to do in Django. We can also plug in our favorite ORM and use it without any risk of unpredictable app behavior. In contrast to Django, it’s easy to integrate NoSQL databases with Flask.
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Do you know any Python projects on Github that are examples of best practices and good architecture?
This may not be exactly what you asked for but I found contributing to open source projects really exposed me to different approaches I never would have considered and may not have fully grasped had I not had to actually dive into the code to solve an issue. Falcon is a great place to start and the guys are super friendly there.
- Falcon 3.0 released!
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Designing rest APIs as a data engineer
https://falcon.readthedocs.io/en/stable/ https://fastapi.tiangolo.com/
What are some alternatives?
CrossHair - An analysis tool for Python that blurs the line between testing and type systems.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
EvoMaster - The first open-source AI-driven tool for automatically generating system-level test cases (also known as fuzzing) for web/enterprise applications. Currently targeting whitebox and blackbox testing of Web APIs, like REST, GraphQL and RPC (e.g., gRPC and Thrift).
hug - Embrace the APIs of the future. Hug aims to make developing APIs as simple as possible, but no simpler.
klara - Automatic test case generation for python and static analysis library
Dependency Injector - Dependency injection framework for Python
icontract-hypothesis - Combine contracts and automatic testing.
connexion - Connexion is a modern Python web framework that makes spec-first and api-first development easy.
methods2test - methods2test is a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java software repositories
apistar - The Web API toolkit. 🛠
code - Example application code for the python architecture book
restless - A lightweight REST miniframework for Python.