openapi-python-client VS hypothesis

On the backend, developers either need to manually document the entire API or rely on auto-generation tools that don’t fully meet their needs. Consumers face the same choice, write code by hand or workaround the bugs in their SDK generator (stated, lovingly, as the maintainer of an OpenAPI client generator). On top of this, these solutions result in inconsistent understandings of the API. Reproducing errors becomes time-consuming and frustrating, which feels like a battle instead of a collaboration. What we need is a shared language to describe how the API works—one that doesn’t add unnecessary layers of abstraction or manual work.

Has anyone tried Kiota, specifically the Python support? How does it compare to https://github.com/openapi-generators/openapi-python-client ?

I think we use these - https://github.com/openapi-generators/openapi-python-client

Thanks for the link, but not necessarily.

How WSDL and the code generation around it worked, was that you'd have a specification of the web API (much like OpenAPI attempts to do), which you could feed into any number of code generators, to get output code which has no coupling to the actual generator at runtime, whereas Pyotr is geared more towards validation and goes into the opposite direction: https://pyotr.readthedocs.io/en/latest/client/

The best analogy that i can think of is how you can also do schema first application development - you do your SQL migrations (ideally in an automated way as well) and then just run a command locally to generate all of the data access classes and/or models for your database tables within your application. That way, you save your time for 80% of the boring and repetitive stuff while minimizing the risks of human error and inconsistencies, while nothing preventing you from altering the generated code if you have specific needs (outside of needing to make it non overrideable, for example, a child class of a generated class). Of course, there's no reason why this can't be applied to server code either - write the spec first and generate stubs for endpoints that you'll just fill out.

Similarly there shouldn't be a need for a special client to generate stubs for OpenAPI, the closest that Python in particular has for now is this https://github.com/openapi-generators/openapi-python-client

However, for some reason, model driven development never really took off, outside of niche frameworks, like JHipster: https://www.jhipster.tech/

Furthermore, for whatever reason formal specs for REST APIs also never really got popular and aren't regarded as the standard, which to me seems silly: every bit of client code that you write will need a specific version to work against, which should be formalized.

Tallying up the results, we get 7/8 "MUST" requirements met. I think that Paperclip + actix-web seems like the most promising candidate. I'm really not opposed to writing the OpenAPI v3 construction myself as I've worked with the structure a fair bit in my openapi-python-client project (shameless plug).

Automatic documentation via OpenAPI, which lets you do things like generate Python code that knows how to talk to your API.

Hypothesis for Property-Based Testing: Hypothesis is a Python library facilitating property-based testing. It offers a distinct advantage by generating a wide array of input data based on specified properties or invariants within the code. The perks of Hypothesis include:

But then add tests! Tests for LaTeX equations that had never been executable as code.

https://github.com/HypothesisWorks/hypothesis :

> Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.

> This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck, but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.

pgregory.net/rapid is a modern Go property-based testing library initially inspired by the power and convenience of Python's Hypothesis.

Hypothesis to generate dummy data for test.

To create your own test cases, I recommend you use hypothesis-graphql in combination with hypothesis. hypothesis is a property-based testing library. Property-based testing is an approach to testing in which you make assertions about the result of a test given certain conditions and parameters. For example, if you have a mutation that requires a boolean parameter, you can assert that the client will receive an error if it sends a different type. hypothesis-graphql is a GraphQL testing library that knows how to use hypothesis strategies to generate query documents.

The Hypothesis stateful testing code is somewhat self-contained, since it mostly builds on top of internal APIs that already existed.

We've had a lot of success combining that approach with property-based testing (https://github.com/HypothesisWorks/hypothesis) for the query engine at backtrace: https://engineering.backtrace.io/2020-03-11-how-hard-is-it-t... .

Systems I've used for this include https://agda.readthedocs.io/en/v2.6.0.1/getting-started/what... https://coq.inria.fr https://www.idris-lang.org and https://isabelle.in.tum.de

An easier alternative is to try disproving the statement, by executing it on thousands of examples and seeing if any fail. That gives us less confidence than a full proof, but can still be better than traditional "there exists" tests. This is called property checking or property-based testing. Systems I've used for this include https://hypothesis.works https://hackage.haskell.org/package/QuickCheck https://scalacheck.org and https://jsverify.github.io