Schemathesis
bootswatch
Schemathesis | bootswatch | |
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23 | 22 | |
2,091 | 14,432 | |
1.6% | - | |
9.7 | 7.8 | |
10 days ago | 9 days ago | |
Python | JavaScript | |
MIT License | MIT License |
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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...
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Show HN: Auto-generate load tests/synthetic test data from OpenAPI spec/HAR file
Why is AI needed for this at all? Have you heard about Schemathesis (https://github.com/schemathesis/schemathesis)?
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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.
[0] https://stoplight.io/
[1] https://koxudaxi.github.io/datamodel-code-generator/
[2] https://pyapi-server.readthedocs.io
[3] https://schemathesis.readthedocs.io
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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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How do you manage microservices API versions and branching strategies?
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.
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This Week in Python
schemathesis – Run generated test scenarios based on your OpenAPI specification
bootswatch
- JHipster 8 - Criando uma aplicação monolítica
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100+ FREE Resources Every Web Developer Must Try
Bootswatch
- Bootstrap Plug-and-Play Theme
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I made a tool that takes any Pokemon and makes a colour palette out of it! (for web devs) v2
The colors primarily, I suppose. Something similar to what you see in https://bootswatch.com/.
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*very new to using R on posit cloud* | is there a list of themes that can be put in the “theme:” function anywhere? i haven’t been able to find any online. just curious! i like to make my docs look pretty :)
That ^ . And I’ve found the Bootswatch page a very helpful visual aid. https://bootswatch.com/
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Update 1.6 for Bucket budgeting app OpenBudgeteer
Themes are now supported. Visit Bootswatch for more details. To select a theme use the new option APPSETTINGS_THEME as docker environment variable or in the appsettings.json file.
- A bootstrap theme which isn't for dashboards?
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How should I style my django app?
Others have said Bootstrap already, let me just chime in and point you towards bootswatch (https://bootswatch.com/) , they have great, free themes with great examples, I basically learned bootstrap in large parts by copying example code from their site and tweaking it to my needs / dissecting and trying to understand it.
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How important are website looks/graphics over functionality?
One very quick thing you can do is to take a look at bootstrap themes. One site which offers them is Bootswatch.
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Comparison of UI libraries for React
I’ve found Bootswatch is a very quick way of making bootstrap more visually interesting
What are some alternatives?
dredd - Language-agnostic HTTP API Testing Tool
Bootstrap - The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
Robot Framework - Generic automation framework for acceptance testing and RPA
Pure - A set of small, responsive CSS modules that you can use in every web project.
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
NES.css - NES-style CSS Framework | ファミコン風CSSフレームワーク
coverage
react-bootstrap - Bootstrap components built with React
drf-openapi-tester - Test utility for validating OpenAPI documentation
test - Cleanest skin for the steam client
tox - Command line driven CI frontend and development task automation tool.
Bulma - Modern CSS framework based on Flexbox