pytest vs Goconvey comparison of testing frameworks
What are the differences between pytest and Goconvey?

pytest

https://docs.pytest.org/en/latest/

Goconvey

https://github.com/smartystreets/goconvey
Programming language

Python

Go

Category

Unit Testing

Regression Testing, Unit Testing

General info

Pytest is the TDD 'all in one' testing framework for Python

Pytest is a powerful Python testing framework that can test all and levels of software. It is considered by many to be the best testing framework in Python with many projects on the internet having switched to it from other frameworks, including Mozilla and Dropbox. This is due to its many powerful features such as ‘assert‘ rewriting, a third-party plugin model and a powerful yet simple fixture model.

BDD style testing framework for Go

Goconvey is a two pronged testing tool consisting of a test runner that watches your code for changes, runs 'go test' and renders your results in a web browser and the second a library that allows you to write BDD-style tests with standard 'go test' functions
xUnit
Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.

No

No

Client-side
Allows testing code execution on the client, such as a web browser

Yes

pytest can test any part of the stack including front-end components

Yes

Yes, Goconvey can perform front-end tests
Server-side
Allows testing the bahovior of a server-side code

Yes

pytest is powerful enough to test database and server components and functionality

Yes

Yes one can perform end-to-end HTTP tests with goconvey to see how an application works against remote servers
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

Yes

Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.

Yes

Yes, Goconvey uses scopes to define fixtures and a reset function for teardown
Group fixtures
Allows defining a fixed, specific states of data for a group of tests (group-fixtures). This ensures specific environment for a given group of tests.

Yes

Pytest's powerful fixture model allows grouping of fixtures

Yes

One can define group fixtures using scopes
Generators
Supports data generators for tests. Data generators generate input data for test. The test is then run for each input data produced in this way.

Yes

pytest has a hook function called pytest_generate_tests hook which is called when collecting a test function and one can use it to generate data

Yes

The web UI has a button to open the built in generator
Licence
Licence type governing the use and redistribution of the software

MIT License

Goconvey License

Mocks
Mocks are objects that simulate the behavior of real objects. Using mocks allows testing some part of the code in isolation (with other parts mocked when needed)

Yes

By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest

Yes

Using the mock package or mockery library to autogenerate mock code
Grouping
Allows organizing tests in groups

Yes

Tests can be grouped with pytest by use of markers which are applied to various tests and one can run tests with the marker applied

Yes

Similar to a table driven approach an entire suite can be contained in a single function
Other
Other useful information about the testing framework