Schemathesis
pytest
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Schemathesis | pytest | |
---|---|---|
23 | 30 | |
2,058 | 11,239 | |
3.2% | 1.9% | |
9.7 | 9.9 | |
4 days ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
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.
Schemathesis
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Ask HN: Any Good Fuzzer for gRPC?
I am not aware of any tools like that, but eventually, I plan to add support for gRPC fuzzing to Schemathesis. There were already some discussions and it is more or less clear how to move forward. See https://github.com/schemathesis/schemathesis/discussions/190...
I have been using Schemathesis (https://github.com/schemathesis/schemathesis) for some time to test REST APIs and I have found it amazing. I love the ways it find unexpected bugs and it really help me have more confidence in my systems.I also love how it integrate directly with pytest but that's more of a cherry on the cake.
But now I am working with GRPC service I can't find anything similar. The few solutions I found are closed source and necessitate to integrate with 3rd party platforms.I found that strange as GRPC as RPC seem perfect for fuzzing.
Did I miss a great tool that you use?
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
SchemaThesis is a powerful tool, especially when working with web APIs, and here's how it can enhance your testing capabilities:
- Hurl 4.0.0
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OpenAPI v4 Proposal
I'm sorry, but you have completely misunderstood the purpose of Open API.
It is not a specification to define your business logic classes and objects -- either client or server side. Its goal is to define the interface of an API, and to provide a single source of truth that requests and responses can be validated against. It contains everything you need to know to make requests to an API; code generation is nice to have (and I use it myself, but mainly on the server side, for routing and validation), but not something required or expected from OpenAPI
For what it's worth, my personal preferred workflow to build an API is as follows:
1. Build the OpenAPI spec first. A smaller spec could easily be done by hand, but I prefer using a design tool like Stoplight [0]; it has the best Web-based OpenAPI (and JSON Schema) editor I have encountered, and integrates with git nearly flawlessly.
2. Use an automated tool to generate the API code implementation. Again, a static generation tool such as datamodel-code-generator [1] (which generates Pydantic models) would suffice, but for Python I prefer the dynamic request routing and validation provided by pyapi-server [2].
3. Finally, I use automated testing tools such as schemathesis [3] to test the implementation against the specification.
[1] https://koxudaxi.github.io/datamodel-code-generator/
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Faster time-to-market with API-first
Consolidating the API specification with OpenAPI was a turning point for the project. From that moment we were able to run mock servers to build and test the UI before integrating with the backend, and we were able to validate the backend implementation against the specification. We used prism to run mock servers, and Dredd to validate the server implementation (these days I’d rather use schemathesis).
- Show HN: Step CI – API Testing and Monitoring Made Simple
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API-first development maturity framework
In this approach, you produce an API specification first, then you build the API against the specification, and then you validate your implementation against the specification using automated API testing tools. This is the most reliable approach for building API servers, since it’s the only one that holds the server accountable and validates the implementation against the source of truth. Unfortunately, this approach isn’t as common as it should be. One of the reasons why it isn’t so common is because it requires you to produce the API specification first, which, as we saw earlier, puts off many developers who don’t know how to work with OpenAPI. However, like I said before, generating OpenAPI specifications doesn’t need to be painful since you can use tools for that. In this approach, you use automated API testing tools to validate your implementation. Tools like Dredd and schemathesis. These tools work by parsing your API specification and automatically generating tests that ensure your implementation complies with the specification. They look at every aspect of your API implementation, including use of headers, status codes, compliance with schemas, and so on. The most advanced of these tools at the moment is schemathesis, which I highly encourage you to check out.
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This Week in Python
schemathesis – Run generated test scenarios based on your OpenAPI specification
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Best way to test GraphQL API using Python?
Hi u/autumn_nite Python has an excellent ecosystem for GraphQL testing. Your first stop for painless GraphQL testing is schemathesis. To test your API with schemathesis, you simply need to start up your GraphQL server, and then run schemathesis like this:
pytest
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The Uncreative Software Engineer's Compendium to Testing
Pytest: is a third-party testing framework that supports fixtures, parameterized testing, and easy test discovery while having room to add plugins to extend its functionality.
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pytest VS vedro - a user suggested alternative
2 projects | 16 Jul 2023
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TDD vs BDD - A Detailed Guide
Next, you need to install a testing framework that will be used for performing unit testing in your project. Several testing frameworks are available depending on the programming language used to create an application. For example, JUnit is commonly used for Java apps, pytest for Python apps, NUnit for .NET apps, Jest for JavaScript apps, and so on. We’ll use the Jest framework for this tutorial since we are using JavaScript.
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Is there a way to automate testing in python? In my case :
Yea, read through the pytest docs.
- Testing an automation framework
- 2023 Development Tool Map
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How to raise the quality of scientific Jupyter notebooks
Since ITK's inception in 1999, there has been a focus on engineering practices that result in high-quality software. High-quality scientific software is driven by regression testing. The ITK project supported the development of CTest and CDash unit testing and software quality dashboard tools for use with the CMake build system. In the Python programming language, the pytest test driver helps developers write small, readable scripts that ensure their software will continue to work as expected. However, pytest can only test Python scripts by default, and errors in untested computational notebooks are more common than well-tested Python code.
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Getting Started with a Web Scraping Project
Inorder to have this project be as well-rounded as possible we'll code a series of tests to test the code using pytest. Create a new directory called tests and create a new file called test_db.py in the tests directory. Inside create a test_players.py file.
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Python Malware Starting to Employ Anti-Debug Techniques
that doesn't make much sense and there are necessary uses for eval() /exec(), mostly for dynamic creation of code:
For example here's Python dataclasses in the standard library using exec() to create the `__init__` and other methods that go on your dataclass:
https://github.com/python/cpython/blob/main/Lib/dataclasses....
Here's Pydantic using it for a jupyter notebook check:
https://github.com/pydantic/pydantic/blob/594effa279668bd955...
here's Pytest using it to rewrite modules so that functions like assert etc. are instrumented by pytest:
https://github.com/pytest-dev/pytest/blob/eca93db05b6c5ec101...
Here's the decorator module using it (as is the only way to do this in Python) to create a signature matching decorator for an arbitrary function:
https://github.com/micheles/decorator/blob/ad013a2c1ad796996...
All of these libraries are completely secure as eval/exec are used with code fragments that are generated by the libraries, not based on untrusted input.
eval() /exec() are not running executable files, just Python code, the same way all the rest of the package is already doing.
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Willans' Formula
For brevity we will test the numbers from 0 through 10, below are a collection of pytest tests covering these cases. Thus, in test_willans.py we have the following:
What are some alternatives?
nose2 - The successor to nose, based on unittest2
Robot Framework - Generic automation framework for acceptance testing and RPA
Behave - BDD, Python style.
Slash - The Slash testing infrastructure
hypothesis - Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
nose - nose is nicer testing for python
mamba - The definitive testing tool for Python. Born under the banner of Behavior Driven Development (BDD).
dredd - Language-agnostic HTTP API Testing Tool
PyRestTest - Python Rest Testing
tox
coverage