(This article was originally posted on Medium.)
I’ve been using Swagger/OpenAPI for a few years now, and RAML before that. I’m a big fan of these “API documentation” tools because they provide a number of additional benefits beyond simply being able to generate nice-looking documentation for customers and keep client-side and server-side development teams on the same page. However, many projects fail to fully realize the potential of OpenAPI because they approach it the way they approach Javadoc or JSDoc: they add it to their code, instead of using it as an API design tool.
Here are five reasons why generating OpenAPI specifications from code is a bad idea.
You wind up with a poorer API design when you fail to design your API.
You do actually design your API, right? It seems pretty obvious, but in order to produce a high-quality API, you need to put in some up-front design work before you start writing code. If you don’t know what data objects your application will need or how you do and don’t want to allow API consumers to manipulate those objects, you can’t produce a quality API design.
OpenAPI gives you a lightweight, easy to understand way to describe what those objects are at a high level and what the relationships are between those objects without getting bogged down in the details of how they’ll be represented in a database. Separating your API object definitions from the back-end code that implements them also helps you break another anti-pattern: deriving your API object model from your database object model. Similarly, it helps you to “think in REST” by separating the semantics of invoking the API from the operations themselves. For example, a user (noun) can’t log in (verb), because the “log in” verb doesn’t exist in REST — you’d create (POST) a session resource instead. In this case, limiting the vocabulary you have to work with results in a better design.
It takes longer to get development teams moving when you start with code.
It’s simply quicker to rough out an API by writing OpenAPI YAML than it is to start creating and annotating Java classes or writing and decorating Express stubs. All it takes to generate basic sample data out of an OpenAPI-generated API is to fill out the example property for each field. Code generators are available for just about every mainstream client and server-side development platform you can think of, and you can easily integrate those generators into your build workflow or CI pipeline. You can have skeleton codebases for both your client and server-side plus sample data with little more than a properly configured CI pipeline and a YAML file.
You’ll wind up reworking the API more often when you start with code.
This is really a side-effect of #1, above. If your API grows organically from your implementation, you’re going to eventually hit a point where you want to reorganize things to make the API easier to use. Is it possible to have enough discipline to avoid this pitfall? Maybe, but I haven’t seen it in the wild.
It’s harder to rework your API design when you find a problem with it.
If you want to move things around in a code-first API, you have to go into your code, find all of the affected paths or objects, and rework them individually. Then test. If you’re good, lucky, or your API is small enough, maybe that’s not a huge amount of work or risk. If you’re at this point at all, though, it’s likely that you’ve got some spaghetti on your hands that you need to straighten out. If you started with OpenAPI, you simply update your paths and objects in the YAML file and re-generate the API. As long as your tags and operation Ids have remained consistent, and you’ve used some mechanism to separate hand-written code from generated code, all you’re left to change is business logic and the mapping of the API’s object model to its representation in the database.
The bigger your team, the more single-threaded your API development workflow becomes.
In larger teams building in mixed development environments, it’s likely you have people who specialize in client-side versus server-side development. So, what happens when you need to add to or change your API? Well, typically your server-side developer makes the changes to the API before handing it off to the client-side developer to build against. Or, you exchange a few emails, each developer goes off to do his own thing, and you hope that when everyone’s done that the client implementation matches up with the server implementation. In a setting where the team reviews the proposed changes to the API before moving forward with implementation, you’re in a situation where code you write might be thrown away if the team decides to go in a different direction than the developer proposed.
It’s easy to avoid this if you start with the OpenAPI definition. It’s faster to sketch out the changes and easier for the rest of the team to review. They can read the YAML, or they can read HTML-formatted documentation generated from the YAML. If changes need to be made, they can be made quickly without throwing away any code. Finally, any developer can make changes to the design. You don’t have to know the server-side implementation language to contribute to the API. Once approved, your CI pipeline or build process will generate stubs and mock data so that everyone can get started on their piece of the implementation right away.
The quality of your generated documentation is worse.
Developers are lazy documenters. We just are. If it doesn’t make the code run, we don’t want to do it. That leads us to omit or skimp on documentation, skip the example values, and generally speaking weasel out of work that seems unimportant, but really isn’t. Writing OpenAPI YAML is just less work than decorating code with annotations that don’t contribute to its function.
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The work doesn’t end when the developers have delivered. Deploying and managing development, QA, and production DLT networks is seriously challenging. DLT architectures include a number of components, deployed across a number of physical or virtual machines, scaled across a number of identical nodes. Aviator aims to have IT systems administrators and managers covered there as well. We’re working on a toolset for visually designing your DLT network infrastructure, and a way to automatically deploy that design to your physical or virtual hardware. We’ll be delivering tooling to monitor and manage those assets through our own management tooling, or by integrating into the network management tooling your enterprise may already have. This is an area where even the most mature DLT platforms struggle, and there are exciting opportunities to lower frictions when managing DLT networks through better management capabilities.
For more information on Aviator, please visit our website at 
