Selenium vs Specter comparison of testing frameworks
What are the differences between Selenium and Specter?

Selenium

https://pypi.org/project/selenium/

Specter

http://specter.sourceforge.net/
Programming language

Python

.NET

Category

Web Automation

Acceptance Testing

General info

Selenium is an open source tool used to test web applications

Selenium is a powerful testing tool which can send standard Python commands to different browsers, despite variations in browser design. It also provides extensions to emulate user interaction with browsers, a distribution server for scaling browser allocation, and the infrastructure for implementations of the W3C WebDriver specification that lets you write interchangeable code for all major web browsers

Specter is a behaviour-driven development framework for .NET and Mono

Specter enables behavior driven development (BDD) by allowing developers to write executable specifications for their objects, before actually implementing them, this is similar to test driven development however the different nomenclature makes it different from writing 'tests' for code that does not exist yet
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

It is primarily a browser automation tool which tests front-end components and functionality

Yes

Developers can create specfications of the expected front-end behaviours and test them
Server-side
Allows testing the bahovior of a server-side code

Yes

It can perform Unit tests and can test various components and behaviours in the backend using a BDD or TDD approach

Yes

Yes developers can create specfications of the expected back-end behaviours and test these.
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

Yes

By writing your Selenium WebDriver tests in PyTest, this gives you access to Pytest's powerful fixture model

N/A

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

One can group fixtures if accessing Pytest's fixture model

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

By using a library such as Faker or Fake-factory

N/A

Licence
Licence type governing the use and redistribution of the software

Apache License 2.0

BSD 3-Clause 'New' or 'Revised' 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

It includes support for mocking

N/A

Grouping
Allows organizing tests in groups

Yes

By using the TestNG feature with which we can create groups and maintain them easily

Yes

You can create your own test suites with specter
Other
Other useful information about the testing framework