faker
dredd
faker | dredd | |
---|---|---|
9 | 15 | |
17,101 | 4,129 | |
- | 0.3% | |
9.5 | 2.4 | |
7 days ago | 6 months ago | |
Python | JavaScript | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
faker
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Leveling up your custom fake data with Faker.js
Faker was originally written in Perl and is also available as a library for Ruby, Java, and Python.
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The Uncreative Software Engineer's Compendium to Testing
Faker: a library that generates fake data that can be useful when you need data to test for various components.
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Exploring LLMs for Data Synthesizing & Anonymization: looking for Insights on Current & Future Solutions
Don't get me wrong, LLMs are awesome but totally unsuited for what you are describing. Classic data science tools like faker will be better for the task in pretty much every aspect. They can generate synthetic datasets and anonymize existing ones faster and far more reliable than any LLM.
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Undercover work
The Python package, Faker, is just what you're looking for!
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Is there a way to automate testing in python? In my case :
for datatypes like string/date and other stuff, there is a Python library called faker, which you can use. It can generate fake names, fake phone numbers, dates, and addresses. here is the link to the documentation. https://faker.readthedocs.io/ here is a link to a blog post explaining Faker. https://levelup.gitconnected.com/pythons-faker-library-your-go-to-solution-for-test-data-generation-3a070065cc04
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Testing files in Python like a pro
Then test cases became more complex. Primary data sources were often files. We needed to test pipelines. Faker still helped a lot, but it was not convenient to copy your last-best-approach for files and reinvent the wheel over and over with each project.
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Database automation challenges and how to solve them
For a cloud-based solution, one can write their own Terraform or CloudFormation for installation as soon as their RDS instance boots up with appropriate security and authentication details. For a local dev environment, one can rely on Faker to create mock database data for your database.
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How to create a 1M record table with a single query
Creating realistic fake data is useful in lower environments and for load testing. Outside of SQL I like faker: https://github.com/joke2k/faker
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DuckDB: an embedded DB for data wrangling
To test a database, first you need some data. So I created a python script and used Faker to create the following CSV files:
dredd
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The Uncreative Software Engineer's Compendium to Testing
Dredd: used to test APIs based on the API blueprint or OpenAPI specification, to ensure implementation matches the specification.
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Tool for generating example API requests and responses from OpenAPI
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.
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Integration testing best practices for API servers...
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.
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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).
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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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What advice you could give to BEGINNER?
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.
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Dealing with backend developers
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.
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what are the best tools for documenting apis?
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.
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How bad models ruin an API (or why design-first is the way to go)
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.
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Is it possible to automate Api testing without writing any aditional code ?
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.
What are some alternatives?
Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
FauxFactory - Generates random data for your tests.
prism - Turn any OpenAPI2/3 and Postman Collection file into an API server with mocking, transformations and validations.
fake2db - create custom test databases that are populated with fake data
postman-app-support - Postman is an API platform for building and using APIs. Postman simplifies each step of the API lifecycle and streamlines collaboration so you can create better APIs—faster.
picka - pip install picka - Picka is a python based data generation and randomization module which aims to increase coverage by increasing the amount of tests you _dont_ have to write by hand.
redoc - 📘 OpenAPI/Swagger-generated API Reference Documentation
PyRestTest - Python Rest Testing
ava - Node.js test runner that lets you develop with confidence 🚀
radar
portman - Port OpenAPI Specs to Postman Collections, inject test suite and run via Newman 👨🏽🚀