Python for test automation: formatting and linting

Code formatting and linting offer several benefits in software development:

1. Consistency: Consistently formatted code enhances readability and maintainability. By enforcing a consistent coding style across the codebase, it becomes easier for developers to understand each other's code, leading to better collaboration and reduced cognitive overhead when navigating through the code.
2. Readability: Well-formatted code is easier to read and understand, even for developers who are new to the project or are reviewing the code for the first time. Code formatting rules help in organizing the code structure, indentation, and alignment of elements, making it more intuitive to follow the flow of logic.
3. Reduced Bugs: Linters can detect potential coding errors, bugs, or anti-patterns in the codebase. They analyze the code statically and provide feedback on issues such as syntax errors, unused variables, undefined functions, or potential runtime errors, helping developers catch and fix these issues early in the development process.
4. Enforced Best Practices: Code linting tools can enforce coding standards, best practices, and style guidelines specific to the programming language or project. By adhering to established coding conventions and standards, developers can produce higher-quality code that is consistent, maintainable, and less prone to errors.

And more. In Python these benefits can largely be achieved by simply setting up automatic code formatting with black, which is a Python package available to be installed with pip. We can also install another package, pylint, to check our code for potential errors and other issues in our code. However, I have found that using pylint can become a detriment when it is followed too strictly. I can wholeheartedly recommend automatic code formatting. It makes it much less tedious to write consistent code.