ansi-test VS Schemathesis

I have applied it to testing Common Lisp implementations, but there has been much work on applying it to other languages (most famously C and Javascript.)

I applied the following techniques:

(1) Generate random valid well defined programs and see (a) if they crash the compiler, (b) cause different CL implementations to produce different outputs, (c) when modified (by addition of randomly generated optimization directives or valid type declarations) they still produce the same output. This is differential testing, which was used by McKeeman at DEC in the 1990s to test C compilers, and later improved (again, on C compilers) by Yang, Chan, Eide, and Regehr at U. of Utah (csmith, creduce).

Since a running lisp image can generate and compile functions internally (this IS lisp, after all), the testing loop can be very fast. Since 2003 I have run this on and off for many billions of iterations on desktop and laptop machines on various CL implementations, now mainly on SBCL. Most of the test input reduction is handled automatically, which is a big help.

(2) Generate random possibly invalid code by mutating or recombining snippets drawn from a large corpus of code, to see if it crashes the compiler (in CL implementations where the compiler is promised to never respond to bad code by signaling an error.) This was also the approach jsfunfuzz took on Javascript.

(3) Extensive fuzzing of calls to standard functions in CL, using random generation of input values and random generation of valid type declarations, with the invariant being that the same values should be computed (and the compiler not fail.) This is a specialization of (1), but was sufficiently different that the bugs it found were not the same.

Examples of tests produced by (1) and (3) over an early period when this was being developed. Each caused a failure in some CL implementation: https://github.com/pfdietz/ansi-test/blob/master/misc/misc.l...

See also the various bugs I've reported against SBCL over the years, many of which come from this testing. https://bugs.launchpad.net/~paul-f-dietz

The experience with this sort of testing of compilers (in any language) is that if the compiler (free or commercial) has never been subjected to it, it will immediately find bugs in the compiler.

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