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tags: Programming theory

The Code Simplicity Chart (c)

How to understand if your code is simple or complex.


This is probably the most confusing and the most important topic of computer programming at the same time. There are thousands of articles telling about the importance of writing simple code, and how it is important for debugging, adding new features quickly, for easier refactoring and for catching up by newcomers. Hundreds of widespread phrases were told by famous computer programmers telling how good it is to write simple code.

But there is absolutely zero articles telling what simple code actually is.

This and the following articles are targeting these topics - what simple code is, how to distinguish it from complicated code and how to replace some patterns we use in our daily work with simpler patterns.

What “simple” means here?

“Easily understood or done; presenting no difficulty.”
“Plain, basic, or uncomplicated in form, nature or design; without much decoration or ornamentation.”
– Oxford Dictionary

Simplicity is subjective, as it comes from our ability to understand - our ability to recognize abstractions, subdivide them into smaller parts and to find connections between them. Luckily, the code we write is measurable so we can, to some extent, measure the amount of hoops our brains have to jump through to understand how the program works.

“What does this program do?” is the main question. If it is easy and obvious to answer by reading the code - the code is considered to be simple.

“Simple” does not mean “familiar”

While it is easier to write code using familiar methods, it is just a matter of habit. We learn new things to change our habits. There can be habits which allow to produce less complicated code. If we will learn them, they will be as familiar to us as our current methods but the code we’re creating will be much simpler from the point of view of the more educated persons we will become.

In these articles on simplicity I’m not going to feed you with patterns which can change with new programming languages or which can even become anti-patterns with time.

Let’s go.

Quantity matters

This is the most important part and all other ideas on simplicity grow from this fact. Quantity matters. Be it the number of Lines of Code (LoC), the number of if conditions, the number of variables - it all matters.

It is a widespread fact that people can keep in mind a limited amount of variables. I disagree. Given enough time we can literally place the entire program into our mind and play with it as we want to. However, while working with real-life tasks we always have limited amount of time and limited amount of code to read. In addition, our code is constantly evolving, invalidating the pieces of code we know. We switch between tasks, our attention switches, and now we need to re-read the code to catch things up.

I sometimes meet people who say: “I like to be verbose”. And sometimes I am not lucky enough so I have to read their code and this is hard. Instead of using if/else they’re creating a class hierarchy. Instead of creating a reusable function they’re copy-pasting 90% of their code into several places.

There is a common practice to be more verbose to explain an algorithm with longer variable names or by splitting long expressions into several small statements. There are also exceptional cases when the algorithm is very compressed, and replacing it with a longer version increases readability. But given the same readability, the shorter version is almost always simpler.

The obvious thing is that understanding a part of a program (the smaller amount of code) is easier than understanding the entire program (the bigger amount of code).

So, this is the first “axiom” here: quantity matters.

Axiom - “a rule or principle that most people believe to be true”
– Oxford Dictionary

The Code Simplicity Chart ©

I never saw it written anywhere, but it literally flies in the air. If you know several programming languages implementing different paradigms, this thing becomes pretty obvious. However, nobody wrote it. So I’ve added this © to mark the authorship and to draw your attention to the fact that this thing is actually NEW.

Here it is, from simple to complex:

  • Immutable data
  • Pure function
  • I/O function
  • Shared mutable data
  • Event function

Let’s consider each of the items here.

Immutable data

Immutable data is the simplest possible thing our programs consist of. Be it a constant or some data structure passed into a function as a parameter - if it is immutable then it is simple.

It is simple because it does not change over time. It contains no actions or logic.

It also does not matter with which other functions it is shared with, the data can be considered without thinking about the other application parts, allowing us to write totally isolated pieces of program. It will just never break and we can always rely on it.

In programming languages without support of immutable data a simple agreement to not modify the data structure can do the trick.

Pure function

Pure function is a function that does only one thing - it transforms data.

Pure function is more complex than immutable data because to write a function we need to know the data structure it works on. Complexity just adds, it is not different. Pure functions are also simple and rock solid because they never break anything outside of them and they always produce the same result given the same arguments.

So, here they are - two building blocks that can be used to write code that is actually scalable.

Only zero scales infinitely.

Zero mutation data and zero side-effecting functions. They can be composed and used without limitations or the fear that something will break. We can grow a program to whatever size we want without being afraid that the amount of bugs will start growing exponentially.

When speaking about pure function simplicity, we just mean that even if it is complex, its complexity does not affect other parts of the program.

I/O function

I/O function transforms data but it also should take place at a certain moment in time.

We can call pure functions in any order and any number of times - the result will always be the same. I/O functions do not have such luxury: if we change the order of I/O function calls we will get “Joe!Hello, ” instead of “Hello, Joe!”.

This complexity does not scale well. Once, we have a couple of functions that should be called in a certain order, we have to architect the whole of our program in a way that these functions will never be called in wrong order, otherwise bugs will happen.

Imagine calling closeFile function before calling read. To prevent this, we wrap such functions into one function which does these calls in the right order. We also should not forget to call closeFile. But now we have a more general I/O function and this function must also be called in a specific order.

We may say that when joining two I/O function calls we reduce these function complexity 2 to 1 but it is still not 0. It can become even worse when we have parallel code execution in the program and the I/O function can covertly affect the execution of other functions sharing the same resource.

Shared mutable data

Shared mutable data goes next. It is important that it must be modified in a certain order, and in addition it affects, covertly, execution of other functions.

Shared mutable data is one of the main reasons behind bugs. It is hard to trace all the ways the data can be modified and so it makes program harder to understand. In large programs, mutable data can be shared quite intricately.

For example, we have a list of goods in memory cache and we want to share it across different application screens to avoid looking up database too often. Suddenly, one of the screens decides to apply a filter to the list and removes some goods that it does not need to show. Nice! The next time another screen will want to take the list of goods, it will only get a part of the list while expecting to get all of them. This is a real bug I fixed once. It could never had happened if the list was immutable. And who knows how many similar bugs exist being unnoticed by testers and annoying users with misbehavior or even crashes.

The problem of mutable shared data makes it almost impossible to write reliable multi-threading code. The complexity of the order of data mutation in multi-threading environment becomes unbearable very quickly, so refactoring code to make use of immutable data is the most reasonable thing we can usually do to eliminate race conditions. It usually leads to code that is easier to understand as well, because immutable data encourages writing pure functions that are very easy to understand.

Shared mutable data can sometimes be considered as a couple of I/O functions - one reads and another writes, while the data can be considered as external to our program. This approach even works in some programming languages, but it just adds another layer of complexity on top of shared mutable data.

Event function

Event function has the highest complexity because it can perform several I/O calls, mutate shared data, and the worst - it can also call other event functions.

For the sake of simplicity, I’m joining under this category all the multi-purpose functions which do hell knows what. “Here be dragons”.

The chart is not complete

There are items in-between. We can consider, for example, local function mutable variables or some fancy ways to work with shared mutable data. The chart shows the main milestones. The rest lies somewhere in-between.

There can also be obvious cases when by having one shared mutable variable we can avoid writing one hundred lines of pure functions, but such cases are extremely rare. Quantity matters and if it allows to write less code, then that is cool, but we should also consider how much other code the reader will have to dig through to understand all the possible interactions with the variable, be they direct or mediated, and how often he or she will have to re-read the code if something will be changed.

What’s next?

The application of this chart is a skill that every computer programmer should develop to consider (him|her)self a professional. It boosts productivity and application reliability to the highest levels. Maintenance costs also go down significantly as we have to spend less time on debugging and reading old code.

These principles already took the web development experience by storm, raising the latest wave of popular frameworks.

The story just starts here - we can use these principles for writing most of our software, getting all the benefits of having simpler code, but this time the approach on simplicity is not just based on someone’s opinion - it is logically justified and can be actually measured.


  • Some magnificent ways to apply The Code Simplicity Chart
  • How code simplicity principles evolve the way we do IT
  • Simplicity in programming language design
  • Reducing complexity in fine details
  • Architecture and code simplicity

I’m planning to release one article per couple of weeks, but life is an unpredictable thing.

Ping me on Twitter if you have to wait too much for the next article in the series, this can actually motivate me to write more. :)

Sharing the article also helps! ;)


Thanks to Upasana Shukla for proofreading this article.

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