faker VS coveragepy

Faker was originally written in Perl and is also available as a library for Ruby, Java, and Python.

Faker: a library that generates fake data that can be useful when you need data to test for various components.

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.

The Python package, Faker, is just what you're looking for!

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

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.

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.

Creating realistic fake data is useful in lower environments and for load testing. Outside of SQL I like faker: https://github.com/joke2k/faker

To test a database, first you need some data. So I created a python script and used Faker to create the following CSV files:

Coverage.py is the go-to tool for measuring code coverage of Python programs. Once installed, you can use it with either unittest or pytest.

Code Coverage Analysis assess the code portions tested by the current test suites without altering the code.

The PLASMA lab @ UMass Amherst (home of the Scalene profiler) has released a new version of Slipcover, a super fast code coverage tool for Python. It is by far the fastest code coverage tool: in our tests, its average slowdown is just 5% (compare to the widely used coverage.py, average slowdown 218%!). The latest release performs both line and branch coverage with virtually no overhead. Use it to dramatically speed up your tests and continuous integration!

Tests ensure the tested behavior is maintained. It's up to the developers to write tests with sufficient coverage. Determining which lines of code on your project are covered by tests is easily quantifiable using tooling. E.g. https://coverage.readthedocs.io/

Taking the idea further, if you build robust tests for your API, you could use a dynamic code analyzer like coverage or figleaf to identify and delete unused functions.

coverage

FWIW, ternary expressions aren't properly detected by coverage: https://github.com/nedbat/coveragepy/issues/509