fakedata-quickcheck
faker
| fakedata-quickcheck | faker | |
|---|---|---|
| - | 9 | |
| 0 | 17,101 | |
| - | - | |
| 0.0 | 9.5 | |
| almost 3 years ago | 7 days ago | |
| Haskell | Python | |
| MIT License | MIT License |
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fakedata-quickcheck
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Tracking mentions began in Dec 2020.
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:
What are some alternatives?
fakedata - Haskell Library for producing quality fake data
Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
faker - A library for generating fake data such as names, addresses, and phone numbers. [Moved to: https://github.com/faker-ruby/faker]
FauxFactory - Generates random data for your tests.
Bogus - :card_index: A simple fake data generator for C#, F#, and VB.NET. Based on and ported from the famed faker.js.
fake2db - create custom test databases that are populated with fake data
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.
PyRestTest - Python Rest Testing
radar
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
Selenium Wire - Extends Selenium's Python bindings to give you the ability to inspect requests made by the browser.
Python Testing Crawler - A crawler for automated functional testing of a web application