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Writing maintainable and testable code to enable programmers to be more productive
Machine Learning Engineer
Hardworking, motivated and goal-oriented engineer with 6+ years of programming experience and 2+ years playing significant roles on Machine Learning and Computer Vision projects for clients ranging from small startups up to large international corporations.
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Most of us have already seen something like "Must be able to write Clean Code" in the list of requirements for some data science and/or software development opportunity, but what does it mean, and why is it important?
Robert C. Martin (Uncle Bob), one of the professionals behind the Agile Manifesto (2001), extensively explored the principles of Clean Code on his book Clean Code: A Handbook of Agile Software Craftsmanship, originally published in 2008.
In short, Clean code is about being professional, writing maintainable and testable code to enable programmers to be more productive. It comprises a set of techniques that allows developers to go fast on the long run.
This post sumarizes some of the main topics covered in Uncle Bob's book, but the complete reading is highly encouraged.
Unless we talk about a project in its initial stage, in most scenarios, a lot of the software development work is related to maintenance and extension of existing code, meaning that a lot of time is spent reading them rather than just writing new lines.
Not following the principles of Clean Code can drastically increase the time needed to read and fully understand a piece of code, leading to bottlenecks and large losses. In extreme circumstances the development speed can slow down almost to a halt.
Clean code is a great way to go fast and have productivity on the long run, and in the following section we explore how to write it.
To make this post lighter to read and facilitate future consultations, this section is divided in a few lists roughly summarizing what I believe are the most important and easier to implement principles of Clean Code, leaving denser and more advanced topics, like S.O.L.I.D., for future posts.
Any software is filled with names, and choosing them wisely can take time, but significantly improves code readability. When choosing names for variables and functions, as well as files and directories, keep the following in mind:
# Bad Example
vf = vi + 9.81 * t
# Good Example
gravitational_acceleration = 9.81
final_velocity = initial_velocity + gravitational_acceleration * time_in_seconds
A lot of the aspects taken into account when writing clean functions, also apply when writing clean classes, covered on Chapter 10 of the book.
I will explore that in more detail on a future post about S.O.L.I.D., but in short, some of the key factors to consider when writing functions are:
Comments do not make up for bad code, and they should often be avoided, but if you find yourself in a situation that comments are really necessary, consider the following:
Things can go wrong, but the professional programmer makes sure that the code always does what it is supposed to do. When handling errors, consider the following:
It is very common to work on projects that depend on third-party software, packages and libraries. Sometimes they are bought, sometimes we rely on open source projects, and sometimes we use code developed by colleagues from our own organization.
Either way we must set the boundaries to integrate foreign code into ours. To keep your code clean, consider the following:
A code is only really clean once it is validated with tests, and test code should also follow Clean Code principles already mentioned. When writing tests, also consider:
One last advice. Whenever writing code, always try to follow the Boy Scout rule:
If you consistently follow the principles of Clean Code, you will have:
Last but not least, according to Michael C. Feathers, also involved in the early Agile movement:
“Clean code always looks like it was written by someone who cares.”