Schemathesis VS dredd

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...

Why is AI needed for this at all? Have you heard about Schemathesis (https://github.com/schemathesis/schemathesis)?

SchemaThesis is a powerful tool, especially when working with web APIs, and here's how it can enhance your testing capabilities:

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.

[0] https://stoplight.io/

[1] https://koxudaxi.github.io/datamodel-code-generator/

[2] https://pyapi-server.readthedocs.io

[3] https://schemathesis.readthedocs.io

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).

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.

Keep all API versions in the code Another strategy is to have all the different API versions in the same code. So you may have a folder structure that looks like this: api ├── v1 └── v2 Within the API folder, you have one folder for v1 and another one for v2. Each folder has its own schemas and routes as required by the API version they implement. If you use URL-based versioning, v1 is accessible through the example.com/v1 endpoint or the v1.example.com subdomain (whichever strategy you use), and same for v2. Deprecating a version is a simple as its corresponding folder. In any case, I'd recommend you also validate your API implementations in the CI using something like schemathesis. Schemathesis looks at the API documentation and automatically generates hundreds of tests to make sure you're using the right schemas, status codes, and so on. It works best if you design and document the API before implementing, which allows you to include OpenAPI links and other features.

schemathesis – Run generated test scenarios based on your OpenAPI specification

Dredd: used to test APIs based on the API blueprint or OpenAPI specification, to ensure implementation matches the specification.

Here are three tools that you can use to generate example API requests and responses from OpenAPI specifications. These tools should work well even if your schemas are deeply nested: Nswag (Command Line and GUI): Nswag is a Swagger/OpenAPI toolchain for .NET, TypeScript, and other platforms. It supports code generation, client generation, and API documentation. You can use NswagStudio, which is a graphical interface, or you can use the command line tool called "NSwag.exe" for generating example API requests and responses. GitHub: https://github.com/RicoSuter/NJsonSchema NswagStudio: https://github.com/RicoSuter/NSwag/wiki/NSwagStudio Dredd (Command Line): Dredd is a language-agnostic command-line tool for validating API descriptions against backend implementations. It supports OpenAPI, Swagger, and API Blueprint formats. Dredd can generate example requests and responses and validate whether your API implementation conforms to the API description. GitHub: https://github.com/apiaryio/dredd Documentation: https://dredd.org/en/latest/ Stoplight Studio (GUI): Stoplight Studio is a modern API design and documentation platform that supports OpenAPI and JSON Schema. It allows you to create, edit, and validate OpenAPI specifications and provides a powerful visual interface for generating example API requests and responses. Website: https://stoplight.io/studio/ GitHub: https://github.com/stoplightio/studio These tools should provide you with the ability to generate example API requests and responses from your OpenAPI specifications and handle deeply nested schemas.

If you want to make sure the server implements a certain contract like there's an handler responding to a GET request to /API/what/ever I'd rather use something else. To be completely honest this is a topic I'm currently also searching for a really good solution but what I found so far (and looks promising) is https://dredd.org/ or https://microcks.io/ Both support OpenAPI testing so you can specify the contract as an OpenAPI spec and validate your server against it.

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).

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.

It's missing the greatest API testing classic Dredd! Other than that the best API testing tool I've used so far is schemathesis. It works by looking at your API specification and automatically launching hundreds of tests per endpoint. It also leverages advanced OpenAPI documentation strategies such as links to test the relationship between various endpoints.

One more tip for the backend developers: make sure the API implementation is tested against the API specification using contract-testing tools such as Dredd or Schemathesis. I specially recommend schemathesis as it's a lot more comprehensive. I recommend you run those tests in the CI and require them to pass before they can merge their API changes. This is the only reliable way to ensure the API works as expected.

The other thing you want to make sure is that the server is implementing the API correctly. In this space, you can use tools such as Dredd and schemathesis, which look at the API specification and automatically test the server implementation against it.

Schemaless schemas make testing difficult. Tools like Dredd and Schemathesis rely on your API documentation to generate tests and validate your API responses. A collection of free-form arrays like the above model will pass nearly every test, even if the length of the arrays or their contents are wrong. Schemaless schemas are also useless for API mocking, which is a fundamental part of building reliable API integrations.

Dredd: this is the classic API testing tool and it's been around for years. Dredd works by looking at your API specification and figuring out what tests need to be generated to validate your API implementation. You don't need to write any additional code, although you may want to create your own custom hooks to customise Dredd's behaviour. Dredd hooks are useful for example to test resource endpoints (the likes of /todo/{todo_id}) and to clean up your database from any resources created during the test suite. I wrote a tutorial on how to write Dredd hooks which you may find useful.