Top 5 Python frameworks for automated testing

Since being selected as a programming language in 2018, Python has always been at the top of the major rankings.

Currently, it ranks third in the Tiobe Index, behind Java and C. With the widespread use of this programming language, automated testing frameworks based on Python have also emerged and continue to develop and enrich.

Therefore, developers and testers need to consider many factors when choosing a testing framework for the project at hand, including: the script quality of the framework, the simplicity of the test cases, and possible technical weaknesses in the running modules. In order to avoid “difficulty in choosing”, I have prepared five Python types of automated testing frameworks for you to compare and discuss.

Robot Framework

As one of the most important Python testing frameworks, Robot Framework is mainly used in test-driven development and acceptance.

Although developed in Python, it can also run on .Net-based IronPython and Java-based Jython. At the same time, as a Python framework, Robot is also compatible with platforms such as Windows, MacOS, and Linux.

Using prerequisites

Before using Robot Framework (RF), you need to install Python 2.7.14 and above. I recommend you use Python 3.6.4 to ensure that appropriate comments can be added to the code snippets and to be able to track changes to the program. At the same time, you also need to install the Python package manager –pip.

Of course, you must also download the corresponding development framework, such as PyCharm Community Edition, which is popular among developers. In addition, since the code snippet itself does not depend on any IDE, you can completely use the existing IDE tools at hand.

Advantages

  • By using the keyword-driven-test method, it can help testers easily create readable test cases, thereby simplifying the entire automation process.

  • Users can easily test syntax in their data.

  • Because it is composed of a variety of common tools and testing libraries, it not only has a large ecosystem, but its individual elements can be used in individual projects.

  • The framework is highly extensible due to having many types of APIs.

  • Although not a built-in feature, Robot can use Selenium Grid (see https://www.lambdatest.com/blog/lambdatest-now-live-with-an-online-selenium-grid-for-automated-cross-browser- testing/) to help users run various parallel tests.

Disadvantages

Although it has the above conveniences, Robot Framework is relatively cumbersome in creating customized HTML reports. At most you can use it to generate short reports in xUnit format.

In addition, Robot Framework is not good at parallel testing.

Comparison of Robot’s features with competing products

Since Robot has rich built-in libraries and can use a simpler test-oriented DSL (Domain Specific Language), if you are new to the field of automated testing and lack development experience, it is better to use it as a Python testing framework than Pytest or Pyunit, which will be mentioned below, are easier to get started with. Of course, if you need to develop a complex automation framework, please also use Pytest or other types of Python frameworks.

Pytest

Pytest, suitable for a variety of software testing, is another Python-type automated testing framework.

With its open source and easy-to-learn characteristics, this tool is often used by QA (quality analysis) teams, development teams, individual teams, and various open source projects. In view of the fact that Pytest has practical functions such as “assert rewriting”, many large Internet applications, such as Dropbox and Mozilla, have switched from the unittest (Pyunit) mentioned below to Pytest.

Using prerequisites

In addition to basic Python knowledge, users do not need more technical reserves. In addition, users only need to have a testing device with a command line interface, and have the Python package manager and IDE tools installed for development.

Advantages

  • In the past, developers had to wrap their tests in various large classes. Now, Pytest allows users to write more compact test suites.

  • Other testing tools require developers or testers to use a debugger or log inspection to detect the source of a specific value. When users use Pytest to write test cases, they can store all values in the test case until they learn which values have failed and which values have been asserted.

  • Since there is not much boilerplate code involved, users can easily write and understand various tests.

  • Test fixture (Fixture) functions are often used to add certain parameters to test functions and return different values. In Pytest, you can modularize one fixture by using another. At the same time, users can use multiple fixtures to cover testing to all parameter combinations without rewriting test cases.

  • Pytest developers keep the framework extensible by releasing various useful plugins. For example: pytest-xdist can be used to perform parallel testing without using other testers. At the same time, unit tests can also be parameterized without copying any code.

  • By providing developers with a variety of special routines, it makes writing test cases simpler and less error-prone, while the code becomes shorter and easier to understand.

Disadvantages

The special routines mentioned above also mean that users must give up certain compatibility. Although it is convenient for users to write test cases, these test cases cannot be used with any other testing framework.

Comparison of Pytest’s features with competing products

When it comes to writing functional test cases and developing complex frameworks, Pytest outperforms UnitTest, which will be mentioned below. However, it is similar to Robot Framework and is only suitable for developing simple testing frameworks.

If you are considering using Pytest, please further refer to Test Automation with Pytest and Selenium WebDriver.

UnitTest/PyUnit

UnitTest/PyUnit, inspired by JUnit, is also a standardized Python class automation testing framework for unit testing. Its base class TestCase provides various assertion methods, as well as all cleanup and setup routines. Therefore, every method in a TestCase subclass has “test” as the noun prefix to identify that they can be run as test cases. Users can use the load method and TestSuite class to group and load various tests.

Of course, you can also build a custom test runner through joint use. Just as we use Junit to test Selenium, UnitTest will also use UnitTest-sml-reporting and can generate various XML types of reports.

Using prerequisites

Since UnitTest uses Python by default, we don’t need any prerequisites. In addition to basic knowledge of the Python framework, you can also additionally install pip and IDE tools for development.

Advantages

  • Developers do not need to install any additional modules.

  • UnitTest is a derivative of xUnit and its working principle is very similar to other xUnit frameworks. Therefore, those without strong Python background can get started quickly.

  • Users can run individual test cases in a simpler way. You just need to predetermine the names on the terminal and the framework can flexibly execute tests for various use cases and produce refined output.

  • It is capable of generating various test reports within milliseconds.

Disadvantages

Although the framework commonly uses snake_case to name various Python codes, because it is derived from Junit, it still retains some traditional camelCase naming methods. This can often lead to confusion.

Because it supports too many abstract methods, the purpose of testing the code is sometimes unclear. Requires a lot of boilerplate code.

Comparison of PyUnit’s features with competing products

Similar to the views of some Python developers, I believe that Pytest can train testers to write better automation code in a very compact way, a programming habit. Although UnitTest is Python’s default automated testing framework, its working principle and naming rules are still slightly different from standard Python code. At the same time, it also requires too much boilerplate code. Therefore the framework is not very popular.

Behave

We all know: behavior-driven development (BDD) is a method based on agile software development. It encourages collaboration among developers, business participants, and QA personnel. As another Python testing framework, Behave allows teams to avoid various complex situations and perform BDD testing. In essence, this framework is very similar to SpecFlow and Cucumber and is often used to perform automated testing. Users can write test cases in a simple and easy-to-read language and paste them into the code during their execution. Moreover, those set behavioral specifications and steps can also be reused in other test plans.

Using prerequisites

Anyone with basic knowledge of Python can use Behave. Other prerequisites include:

You must first install Python 2.7.14 and above.

Requires working with Behave through the Python package manager or pip.

Most developers will choose Pycharm as the development environment, but of course you can also choose other IDE tools.

Advantages

  • Because system behavior uses semi-formal language and a domain vocabulary, Behave helps maintain consistent behavior across the organization.

  • Let development teams working on different modules have similar features through coordinated processing.

  • Each functional block it builds is capable of executing various test cases.

  • Because there are more details worthy of user reasoning and thinking, the target product will be better standardized.

  • It enables project managers and stakeholders to have a clearer understanding of the output of development and QA teams based on a similar specification format.

Disadvantages

Only suitable for black box testing.

Comparison of Behave’s features with competing products

Behave is best suited for black-box testing such as simple web testing. However, Behave is not a good choice for integration tests and unit tests with complex scenarios. As an alternative to Behave, some developers and testers recommend using pytest-bdd. This tool combines all the advantages of Pytest and implements various behavior-driven testing scenarios.

If you are considering using Behave, then you can borrow Behave with Selenium (see https://www.lambdatest.com/support/docs/display/TD/Behave + with + Selenium± + Running + Behave + Automation + Automation scripts mentioned in Scripts + on + LambdaTest + Selenium + Grid).

Lettuce

Lettuce is another behavior-driven automation tool based on Cucumber and Python. Lettuce focuses primarily on common tasks that are characteristic of behavior-driven development. Not only is it easy to use, but it also makes the entire testing process smoother and even more interesting.

Using prerequisites

You need to install Python 2.7.14 and above with an IDE. Of course, you can also use Pycharm or any other IDE tool. At the same time, you also need to install the Python package manager.

Advantages

  • Similar to other BDD testing frameworks, Lettuce allows developers to create multiple scenarios and use simple natural language to describe different functions.

  • Development and QA teams can work together in a coordinated manner based on similar specification formats.

  • For black box testing, Lettuce is very suitable for those behavior-driven test cases.

Disadvantages

In order to successfully implement behavior-driven testing, there needs to be continuous communication between the development team, QA personnel, and stakeholders. A lack of this type of communication can create process ambiguity and create problems for teams. Comparison of competing products

In the eyes of ordinary developers and automated testers, Cucumber is more practical in performing BDD testing. In the eyes of Python developers and QA personnel, Pytest-bdd is the best. The power of Pytest is that its framework implements compact and easy-to-understand code, and can be tightly integrated into behavior-driven testing.

Conclusion

To sum up, among the above five Python frameworks for automated testing, Pytest, Robot Framework and UnitTest can be mainly used for functional and unit testing, while Lettuce and Behave are only suitable for behavior-driven testing. Through further comparison, we believe that: for functional testing, Pytest is the best. If you are new to Python-based automated testing, Robot Framework is the best tool to get started. Although its functionality is somewhat limited, it is very easy to use. For Python-based BDD testing, Lettuce and Behave are equally good. However, if you already have some Pytest experience, then please use Pytest-bdd. I hope this article can help you choose a suitable Python testing framework and carry out testing smoothly.

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