Testing is vital to any software development process, including network programming. It helps reduce project costs and provide high-quality code. While it may not be a developer’s favorite part of their work, building tests into the code from the beginning makes it easier to fix bugs.
The Python programming language makes the whole process simple. Due to its versatility and many available frameworks, testing can be almost entirely automated. Python for networking is a great choice - read on to find out the top seven Python testing frameworks.
What is Python?
Python is one of the most popular programming languages. It is high-level, interpreted, and general purpose. Its distinguishing features, which are also the reasons for its popularity, are its code readability and the fact that it is easy to learn. Python syntax is similar to English, which makes the language more user-friendly.
The Python programming language is also very versatile. It can be used in web development, data analysis, machine learning, system automation, and network programming. It also works great for testing, especially in network applications.
Why does Python work for test automation?
Python is a good choice for automatic testing because of its extensive choice of testing frameworks. Due to its popularity, there’s a thriving community of developers who willingly share their ideas and inventions. There are many resources, frameworks, and libraries regarding test frameworks and network programming.
A significant benefit of Python test frameworks in computer network solutions is that they don’t just work on their own. They can be used in combination to complement each other and create complex tests that check the code thoroughly.
It’s important to remember that all of these frameworks are just tools for running tests. Their power comes from the fact that Python allows for the easy coding of tests.
Below you can find a list of the top seven Python testing frameworks for network programming. All of them are open source, easy to install, and make testing simpler. Each of them has its pros and cons. Read on to find the best one for your needs.
Top Python testing frameworks
PyUnit (Unittest)
PyUnit, also called Unittest, is the framework of choice for many developers. It owes its popularity to the fact that it is a part of the standard Python library. It’s also pretty easy to work with, and you don’t need to install anything else to use it. It is one of the simplest solutions on our list, but it is often a good enough tool for the job.
As the name suggests, Unittest works well with unit testing. Its modular nature allows users to test many parts of the code simultaneously without waiting for the previous tests to complete. PyUnit also supports functional testing and API testing. In Python network applications, it is most commonly used for unit tests.
Robot Framework
Robot Framework is a universal test automation tool. It was originally developed by Nokia engineers as a framework for testing network solutions. Its main uses were acceptance tests, end-to-end tests, and regression tests. It’s suited to all kinds of desktop, mobile, and web apps. To run it, you need Python version 3.6 or higher.
The Robot testing framework uses a keyword testing approach to create test cases using human-readable keywords. This and its tabular data syntax make for a simple format, making writing tests easy. This means it can be used even by users with no coding experience.
Robot is equipped with rich libraries, e.g. Selenium, with many available keywords. It is also extensible with many APIs. It’s used for scale acceptance testing and acceptance/test-driven development. It’s also a popular choice for robotic process automation (RPA).
While Robot Framework does provide parallel testing, there might be some inadequacies with customized HTML reports. Otherwise, it’s a good choice when non-developers are supposed to run keyword-driven tests. However, it might not work well if you want to test more complex scenarios - in that case, some extensions and customizations might be necessary.
PyTest
PyTest is another open-source, third-party testing framework. It’s used for various tests - from simple unit tests to more complex functional tests, to end-to-end acceptance tests. This framework may be used for testing APIs, databases, user interfaces, and much more, as it is a flexible, general-use framework.
The PyTest framework is one of the most accessible frameworks to start with. It has quite a few benefits - it can run multiple tests in parallel, and almost 1000 external plugins are available. PyTest can work alongside the Nose2 and Unittest test suites.
Another advantage of this framework is that it can be extended by various plug-ins, such as pytest-randomly, pytest-cov, pytest-django, or pytest-bdd. It requires Python version 3.7 or higher.
Behave
Behave is one of the most popular Python testing frameworks for behavior-driven development (BDD). It is similar to the Cucumber framework, which uses the Ruby programming language.
The Behave framework contains directories with feature files in a plain text format that looks like natural language. It works on any version of Python over 3.3.
Behave supports the Gherkin language, which makes the tests easy to understand even for people with little technical knowledge. Its use of a natural language style encourages collaboration between business participants, QAs, and developers. It bridges the gap between stakeholders and technical workers.
There is one significant disadvantage to this framework. It doesn’t support parallel execution and only works for black box testing. Otherwise, it is a good choice for teams using a BDD approach.
Lettuce
Lettuce is another Python BDD testing framework. It is similar to Behave, also based on the Cucumber framework, and supports the Gherkin language. It is also easy to understand and promotes cooperation between technical and non-technical team members.
Lettuce is often used for black box testing. But it can also be used for other types of tests, for example, server tests or database behavior and interaction tests.
Lettuce lacks a few features that can be found in more mature frameworks; therefore, it is only a good choice for small, low-risk projects. It requires cooperation between all parties of the project: developers, QAs, and managers. So the lettuce framework is the best fit for a small BDD project in a team that prioritizes cooperation among all members.
Nornir
Nornir is a framework explicitly written for network automation. It’s pretty different from the frameworks mentioned so far as it doesn’t use pseudo-language to make itself more readable. The Nornir documentation states that such a solution makes debugging, troubleshooting, and integrating with other systems harder. That’s why Nornir is written in Python to be used with Python.
Using a pure Python framework allows the user to create more complex test scenarios, which is crucial in network programming. Nornir’s functionalities can be extended via plugins. It offers parallel job executions and inventory management, which makes working on extensive networks easier.
PyATS with the Genie library
PyATS stands for Python Automated Test Systems. This is a framework initially developed by Cisco for its internal use but is now available for external users as well. From the very beginning, it was meant to be a network testing framework, and it even has a built-in definition of network topology for testing. It’s designed to be independent of other infrastructures and suitable for rapid Agile development. It’s usable with Mac OS and Linux.
The pyATS framework can be used for various tests: white and black box testing, feature and unit tests, performance and scale testing, and many more. It is a basic tool that can be extended with available libraries.
There are many packages and products that make writing network programming tests easier. One of them is the Genie library. It contains all the tools necessary for networking test automation. Genie encourages reusable libraries and simplifies testing automation.
Network automation libraries
All of the above-mentioned frameworks are general-use tools and work best for network testing when they are adequately expanded with available plugins and libraries. This way, you can ensure you’re using a network-specific Python tool. Here are a few examples of libraries that help with network automation and can also be used for testing:
- Netmiko - a Python library developed to simplify CLI connections to network devices.
- Paramiko - a library that can be used to test connectivity.
- Ncclient - used for client-side scripting and network application development around the NETCONF protocol.
- NAPALM - used for testing in Python network applications, it contains the Mock driver unit test, which is software that imitates the response pattern of another system.
- Scapy - a library allowing for packet manipulation.
Conclusion
Network application development cannot happen without the testing phase. But tests can be just as time-consuming as they are important. That’s why it’s good to use available resources and not reinvent the wheel. It helps streamline the process and saves some time. The Python frameworks mentioned above are great tools for testing network applications. If you’re interested in the topic of Python development, check out our other articles:
