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

pytest

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

SpecFlow

https://specflow.org/
Programming language

Python

.NET

Category

Unit Testing

Acceptance 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.

SpecFlow is a test automation solution for .NET

SpecFlow is a test automation solution for .NET which follows the BDD paradigm, and is part of the Cucumber family. SpecFlow tests are written with Gherkin, using the official Gherkin parser which allows you to write test cases using natural languages and supports over 70 languages.
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

Front-end behaviour is tested. With specflow specifications of the expected behaviours are made and specflow tests against this
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

Back-end behaviour is tested. Specifications of the expected behaviours are made and specflow tests against them
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

BeforeTestRun and AfterTestRun are executed once for each thread which is a limitation of the current architecture.
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

N/A

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

SpecFlow contains a generator component. The SpecFlow IDE integration tries to locate the generator component in your project structure, in order to use the generator version matching the SpecFlow runtime in your project
Licence
Licence type governing the use and redistribution of the software

MIT License

BSD 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

Specflow intergrates well with mock to give it excellent mocking capabilities
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

You can create test suites with specflow
Other
Other useful information about the testing framework