Dawid Ciężarkiewicz (dpc)

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On state pollution
Oct 10, 2016
4 minutes read


I’d like to share an method I’ve developed on how to judge a code. This method uses a metric that I named state pollution.

Note: I am not well versed in academic Computer Science nomenclature and just trying to be precise in my explanation. Be aware that I might be wrong somewhere and please feel free to correct me if I’m naming something wrong or misusing concepts. Maybe someone already described something like that (or better). Please refer me right places if it the case.

First, I use state as a synonym of entropy or amount of information.

State pollution of a fragment of a code is an amount of aggregate visible state that fragment adds to the whole code.

Aggregate visible state is sum of visible state of every executable line of the code.

Visible state of a piece/line of code is the amount of state this code can access (readable visible state) or modify (writeable visible state).

The state is measured in bits. Eg. boolean variable can be only in two states: true or false. This is 1 bit of information. A C-language char has 256 possible values, which is 8 bits.

The following Rust Hello World example has 0-bits of aggregate visible state.

fn main() {
    println!("Hello world!");

The above code has only one effective line of code: println!(...) and that line has no variables to access or modify.

Now let’s say we’re going to add a pointless mutable 8-bit variable:

fn main() {
    let mut x = 0u8;
    println!("Hello world!");

This new line makes it potentially possible for println!(...) to print a text dependent on it’s value. So the aggregate visible state of the code is now 8-bits, and the state pollution introduced by new line is 8-bits.

Not that if mut keyword was removed, println!(...) could potentially print x, but not modify it (as variables are immutable by default in Rust). That would cause aggregate writeable visible state to stay equal to 0, increasing only readable visible state.

Note: this whole entropy calculation is not meant to be precise. It is to be thought of as Big O Notation. It’s the magnitude that is important, not the exact values! To mimic O(...) I’m going to use similar P(...) where P stands for pollution.

I hope the concept is clear now. Now let’s get to while it’s useful. I find state pollution metric to be generalization of many other more-specific ways to judge the code, and corresponding to “good practices” and judgments about the code that people develop with experience.

Why is using enumerator better than an integer

Let’s compare:

enum State {

state : State;


state : uint;

state that is an enumerator have only 3 possible values (entropy = 1.5 bits). state that is just an integer, has a lot more possible values, thus introducing more state pollution.

Why are local values preferable over globals

Most programmers would agree that avoiding global values in any code is a good practice. Using state pollution metric one could say that, a global variable has a P(n*e) where n is number of lines of the whole code and e is a entropy of the variable itself (1-bit for boolean, 8-bit for C-language char, etc.). In contrast local variable has a P(m*e) where m is total number of lines of one function. As n >= m using local variables instead of global is minimizing aggregate visible state pollution

Natural language explanation simple: every global variable in your code makes every line of your code potentially do something different depending on the value of that variable.

Why is static typing preferable over dynamic typing?

In statically-typed languages bool can have only two values. That’s enforced. In dynamically-typed, every variable can potentially have any value of any type. Variables in dynamically-typed languages introduce more state pollution. Obviously code written in dynamically-typed language variables doesn’t always abuse that, but as a possibility - it’s always there, in every variable. Maybe by mistake, maybe on purpose, and change to the code could introduce a new state. So state pollution is there, maybe just discounted a little.

Why are immutable variables better than mutable

The mutable state increases both writeable and readable visible pollution, immutable only the later. With immutable state any code can potentially experience different behavior depending on the state, but at least can’t change the behavior of other places that are polluted with the same state.

Why is encapsulation and abstraction good

Limiting visibility of the state using eg. Go interfaces or Rust traits, or just field and method visibility settings does not completely remove the state, but wraps it in a surface that limits it’s effective pollution: the code exposed to encapsulated or abstracted state can’t directly interfere with it.

Why is functional code preferable over imperative

By nature functional code limits the state pollution. Eg. pure virtual function can only use it’s arguments, so that arguments are the only state that pollutes the function code.

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