Your tasks: Writing readable code

Why is it important to make your code readable?

Code readability contributes to the accessibility and reusability of your code (the “A” and “R” in the FAIR research software principles¹) - once people freely, legally and easily obtain a copy your software - they need to be able to read and understand it to reuse and extend it. So, readable code helps create more reusable scientific software and can empower collaboration between researchers.

In order to develop readable code, we should ask ourselves: “If I re-read this piece of code in fifteen days or one year, will I be able to understand what I have done and why?” Or: “If a new person who has just joined the project reads my code, will they be able to understand what I have written?”

In addition, software requirements will likely change over time because software is dynamic in nature. When these requirements change, a developer (who is not necessarily the same person who wrote the original code) needs to make changes to the software to satisfy these new and emerging needs. They do this by reading the original code to understand it and identify what needs to change. Readable code facilitates the the process of understanding the code, the evolution of the codebase, and saves future developers’ time and effort.

Considerations

• Readable code is easier to understand, maintain, debug and extend (reuse) - saving time and effort.
• Readable code can also make it better and more secure in a way, as it is easier for reviewers to pick out errors.

What practices are there to help me improve the readability of my code?

One of the most important things we can do to make sure our code is readable by others (and ourselves a few months down the line) is to make sure that it is descriptive, cleanly and consistently formatted and uses sensible, descriptive names for variable, function and module names. In order to help us format our code, we generally follow guidelines known as a style guide. A style guide is a set of conventions that we agree upon with our colleagues or community, to ensure that everyone contributing to the same project is producing code which looks similar in style. While a group of developers may choose to write and agree upon a new style guide unique to each project, in practice many programming languages have a single style guide which is adopted almost universally by the communities around the world. For example, see PEP 8 style guide for Python and Google’s R Style Guide.

Another good practice to help readability is to create code that is modular - structured and split into small, reusable functions that are easier to read, understand and test. Functions with a common goal are further grouped into reusable libraries and packages. Use the common code patterns for creating software - design patterns describe a relatively small, well-defined aspect (i.e. functionality) of code intended to leverage an existing concept/solution rather than re-inventing it. This can decrease the time to develop software and increase the quality of the resulting code and its readability.

Expanding on the code structure theme, following conventions on consistent and informative directory structure for your projects will ensure people will immediately know where to find things within your project, especially helpful for long-term research projects or when working in teams. The directory structure for organising your research software project (or research projects in general) involves creating a clear and logical layout for files and data, ensuring easy navigation, collaboration and reproducibility.

Considerations

• Code is read much more often than it is written - style guidelines are intended to improve the readability of code and make it consistent across the wide spectrum of code
• Consistency with a style guide is important but consistency within a project or a module is more important - if you are joining an existing project, look at the existing code and make sure to adopt whatever practices are already in place.

Solutions

Here are some of the practices to follow to make your code more readable:

• Use consistent formatting - consistent indentation and spacing make code easier to understand and parse.
• Break code into sections - use blank lines to separate different parts of your code, like classes and functions.
• Use descriptive names for function, class and variable names that help explain their purpose.
• Keep lines short - avoid long lines, and instead write blocks of lines that are horizontally short and vertically long.
• Use indentation to show the hierarchy of your code and mark the beginning and end of control structures.
• Write informative comments and docstrings for functions to provide more detail about what the code and is doing, transmit understanding and context
• Follow a code style guide for your programming language that is agreed upon by the community and other programmers will find easy to read
• Automate style checks to help ensure your code is consistent
• Many modern IDEs have built-in support for checking conformance to style conventions and they will warn you when you deviate or even autocorrect things for you
• Use code linters - tools that can identify consistency issues, stylistic and even programming errors
• Organise code into reusable and modular functions that achieve a singular purpose and are more easier to understand, test and reuse
• Use existing and well-tested libraries or packages for common functionality and tasks (e.g. reading and writing data in standard formats) to avoid duplication and reimplementing functionality in custom, more error-prone code
• Follow guidelines and conventions on consistent and informative directory structure for your software or research projects - this way, people will immediately know where to find things within your project

Tools and resources

Tool or resource	Description
PEP 8 style guide for Python	Python Enhancement Proposal 8, Style Guide for Python Code
Python code style conventions	A chapter on Python Code Style Conventions from “Intermediate Research Software Development” course
FAIR Research Software Principles	An introduction to the FAIR Principles for research software
Google’s R Style Guide	R Programming Style Guide
Code linters	Static code analysis tool used to flag programming errors, bugs, stylistic errors and suspicious constructs

How to cite this page

To be added.

Credit

The contents of this page have been inspired by the “Tools and Practices for FAIR Research Software” course¹ and the “Intermediate Research Software Development” course[^2] developed by the Software Sustainability Institute.

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