zakirullin / cognitive-load Goto Github PK

Having web app for people to record and share where their cognitive Action Points are spent would help many stubborn devs and (maybe even more important) unaware managers to get what Development eXperience is all about, and how much money and/or potential contributors are they potentially losing every day.

In the Fallout screen above (taken from this guide) the player character has 10 AP until next turn. In development world each turn is a day, and I would say that 7 AP is the most for most people to handle. Unless there is a SOP (standard operating procedure) that you can follow mindlessly. Realistically I would say 4 AP is the limit per task after which people may start to feel tired.

Unlike physical activity, when a person is exhausted mentally, in can be draining and non-recoverable. The task that takes too much AP will deplete them over and over even without taking actual steps and just thinking about them. I observed that on people learning programming and I observe that on myself when I take more than I can chew.

After AP reserve is over, the person falls out. The good design on cognitive load is reduce AP per action (for everybody) and extend AP limit (for specific person). Making things simpler and automating things both can help. Fallout AP can be used as real metric. Not sure like KPI/OKR metric, but probably a metric on its own.

Some time ago I watched on Youtube this talk from Raymond Hettinger about the Mental Game of Python where he gives some examples of cognitive load. If you are interested in the topic you might find it useful.

I've asked ChatGPT to make a summary of the talk:

In the talk "The Mental Game of Python," Raymond Hettinger, a Python core developer, presents ten programming strategies using live examples. Here are a few key points from the talk:

1. Chunking and Aliasing: Hettinger brings up the theory that the human mind can only handle/remember 7 pieces of information at a time, give or take 2. Any more information can cause cognitive overload and errors. Hence, in programming, he emphasizes the importance of ensuring that programmers can use these cognitive 'slots' to improve the code, rather than having to decipher complex logic. This is achieved by modularizing and standardizing through functions, modules, and packages. Hettinger uses Python's random module to highlight the importance of chunking and modular code (source).

2. Problem Solving: Hettinger quotes the Feynman method of problem-solving: write down a clear problem specification; think very, very hard; write down a solution. He demonstrates the strategies of incremental development and solving simpler programs using the example of a tree walker, showing how they can help build programs that solve complex problems (source).

3. DRY Principle: Hettinger also discusses the Don't Repeat Yourself (DRY) principle in the context of object-oriented programming (OOP), classes, and inheritance. He suggests that programmers should manually repeat tasks until patterns emerge before moving code into functions, with examples from file conversion to illustrate his point (source).

4. OOP as a Graph Traversal Problem: Hettinger argues that OOP is essentially a graph traversal problem. Given the richness of the Python ecosystem, he suggests that there's often no need to create new classes. Instead, programmers can identify their current position on the 'graph,' check where they need to go, and use available methods or write new ones to achieve their goals (source).

If you copy and paste the text section by section Grammarly will offer you suggestions.
https://www.grammarly.com/grammar-check

A new star 🌟 is born: https://www.deepl.com/write

The handbook needs a good section on the code documentation. From the top of my head I could only come up with a bad and improved example, but it seems a bit bulky to fit into a handbook.

Well-documented code should shortcut right to the conclusion, instead of reading and interpreting a chunk of code. It should describe all ins, outs and design decisions taken in the piece of code, making it not necessary to read the potentially not very important at the moment implementation. At the same time, it should be concise and free of unnecessary details.

Example of a Bad Code Documentation:

# Function: calculate
# Parameters: x, y
# Returns: result
# Description: This function calculates the sum of two numbers.
def calculate(x, y):
    # add the numbers
    result = x + y
    # return the result
    return result

Explanation:

• The function name "calculate" is not descriptive.
• The parameter names "x" and "y" are not descriptive, making it unclear what kind of values they represent.
• The return value "result" does not indicate what it represents or its purpose.
• The description of the function is redundant and does not provide any additional meaningful information.
• There is no information about possible errors, edge cases, or any other relevant details.

Improved Code Documentation:

def sum_two_numbers(num1, num2):
    """
    Calculate the sum of two numbers.

    Args:
        num1 (int): The first number.
        num2 (int): The second number.

    Returns:
        int: The sum of num1 and num2.

    Raises:
        ValueError: If either num1 or num2 is not a valid integer.

    Example:
        >>> sum_two_numbers(2, 3)
        5
    """
    try:
        result = int(num1) + int(num2)
        return result
    except ValueError:
        raise ValueError("Both arguments must be valid integers.")

Explanation:

• The function name "sum_two_numbers" clearly conveys the purpose of the function.
• The parameter names "num1" and "num2" are more descriptive and indicate that they represent numbers.
• The return value is explicitly defined as an integer and its purpose is clear.
• The documentation includes a description, specifying what the function does.
• The "Raises" section mentions a specific error that can occur and provides a helpful error message.
• An example is provided to demonstrate how to use the function and what output to expect.

In the improved example, the code documentation is more informative, providing better context, details, and examples. It helps developers understand the purpose, usage, and potential errors of the function, promoting better code comprehension and usage.

Hey,

I am myself advocate of onion architecture.

You said:

we gave it all up in favour of the good old dependency inversion principle

I'd like to see an example, as for me, doing onion architecture is mainly "dependency inversion principle" plus keeping DOMAIN, BUSINESS LOGIC (Use cases) and INFRASTRUCTURE in 3 different layers, glued together with "dependency inversion principle".

I'd love to see explanation how "favour of the good old dependency inversion principle" is different from "onion architecture" so I can better understand that section.

best regards,
Maciej

Great read, and I agree with much of the opinions held.

However, this quote becomes the anchor for many of the following opinions/assumptions:

The average person can hold roughly four facts in working memory

Is this based on Miller's Law? If so, the number is seven instead of four.

Hi, I do research about the cognitive factors of programming. I just completed my Ph.D. at Stanford involving studies of cognitive load in program comprehension, as detailed here: https://arxiv.org/abs/2101.06305

Thanks for putting together this document! You bring up many important points about what makes code easier and harder to read. I'm sure that programmers will learn something useful by reading this document and using its ideas to improve their own code. I appreciate that you keep the focus close to working memory — many people (including researchers!) will invoke "cognitive load" to just mean "a thing is hard to think about", rather than the specific meaning "a task requires a person to hold information in their working memory".

However, my concern with this document is that cognitive load is still a very specific cognitive phenomenon about the use of working memory under specific individual, task, and environmental conditions. We have essentially no experimental data about how to optimize programs to minimize cognitive load. But this document presents a lot of programmer folklore under the authority of "reducing cognitive load", which I worry presents a veneer of scientific-ness to a subject that has very little scientific backing. This document presents ideas that I suspect most developers intuitively agree with (composition > inheritance, too many microservices is bad), and then retroactively justifies these with via cognitive load. Readers get to think "ah good, there's science to back up my feelings," but there's no real science there!

Here's two examples from the document that I think misuse the concept of cognitive load.

"Inheritance nightmare"

Ohh, part of the functionality is in BaseController, let's have a look: 🧠+
Basic role mechanics got introduced in GuestController: 🧠++
Things got partially altered in UserController: 🧠+++
Finally we are here, AdminController, let's code stuff! 🧠++++ [..]

Prefer composition over inheritance. We won't go into detail - there's plenty of material out there.

What exactly is being held in a person's memory here? The contents of the function? The name of the class holding the content? The location of the class in the file? A visual representation of the inheritance hierarchy? The details matter! And are these details even held in working memory? Given the names, a person might be able to infer that UserController is a subclass of BaseController, and not need to store that fact in WM.

It sounds like the issue actually being described here is not inheritance, but rather abstraction -- code is separated over a number of functions and modules, but sometimes a person needs to make a cross-cutting change that involves knowledge of all of those separate pieces of code. (This kind of problem is what tools like Code Bubbles try to solve.) There is a working memory story somewhere here, but it's not just about composition versus inheritance! Using this as a "composition is better than inheritance" parable is a misuse of cognitive load.

"Too many small methods, classes or modules"

Mantras like "methods should be shorter than 15 lines of code" or "classes should be small" turned out to be somewhat wrong. [...]

Having too many shallow modules can make it difficult understand the project. Not only do we have to keep in mind each module responsibilities, but also all their interactions. To understand the purpose of a shallow module, we first need to look at the functionality of all the related modules. 🤯

I think this example is just way too abstract to be useful. For example, in theory "shallow modules" could actually reduce cognitive load. If a person internalizes each module, then that module could be a single chunk in working memory. For instance, consider two Rust implementations of a function that computes the minimum of the inverse of a vector of numbers:

fn min_inverse_1(v: Vec<i32>) -> Option<f32> {
  let mut min = None;
  for x in v {
    if x == 0 { 
      continue;
    }
    let n = 1. / (x as f32);
    match min {
      None => min = Some(n),
      Some(n2) => if n < n2 {
        min = Some(n);
      }
    }    
  }
  min
}

fn min_inverse_2(v: Vec<i32>) -> Option<f32> {
  v.into_iter()
    .filter(|&x| x != 0)
    .map(|x| 1. / (x as f32))
    .reduce(f32::min)
}    

min_inverse_2 relies on a system of shallow modules (in a sense). A person reading min_inverse_2 has to understand what into_iter, filter, map, and reduce all mean. The person reading min_inverse_1 only needs to understand the basic features of the language.

However, the fact that min_inverse_2 relies on many external interfaces is not a problem if a person has internalized those definitions. In fact, it is probably easier to see at a glance what its behavior is, and to verify whether it is implemented correctly. Again, that's why I emphasize that cognitive load is heavily dependent on not just the structure of the code, but also the programmer's knowledge and the tools they use.

One other thing... saying that UNIX I/O is "easy to use due to its simple interface" is a very contestable claim. A user of read has to be aware of the entire function specification, which is actually pretty complicated: https://pubs.opengroup.org/onlinepubs/009604599/functions/read.html

In sum...

I would strongly encourage you to consider renaming this the "Code Complexity Handbook", or a comparable title. There is good advice here that reflects the knowledge of experienced software engineers. But the science of working memory applied to programming is far too young to justify the kinds of claims made here.

This you-tube video does an in depth explanation with examples:

The Flaws of Inheritance (CodeAesthetic)
https://www.youtube.com/watch?v=hxGOiiR9ZKg

Might be worth linking to it.

@erni27 suggested a new section here.

That's a very good idea, actually. We can start sharing our thoughts to see what would emerge.

People overcomplicate things for the sake of optimisation so much time. There's one particular real-world case I would like to highlight.

Often times people optimise things not because the code will be slow, but because they think it would be slow. I.e. the root cause of such a behaviour are big numbers and not-so-good understanding of latency numbers on the low level.

Once a team got a task: "implement a feature for items processing". The business had somewhat 100K+ items in the storage.
Simple solution: get those 100K+ items in app's memory, do the job
Complex solution: inject a morphology plugin to a storage along side with a lua script, so to execute the code exclusively on storage's side. Thus avoiding both passing the data over the network and loading all the data in app's memory.

The justification for this was:

1. That's too much data to transfer over the network
2. 100K would take a lot of app's memory, and it would be slow to go through all the items

All of these are imagined things, based on poor understanding of low-level things.

The reality was:

1. The storage and app reside in same cluster
2. It takes 10 ns (0.00001 ms) to send 1 byte over the network
3. An item size is ~50 bytes
4. Sending 100,000 items over the network would take 50,000,000 ns (50 milliseconds)
5. It sure not an issue to load 5 mb worth of data in app's memory
6. The business grew at a very slow rate, those 100K items were accumulated over 8 years
7. The feature is used by the admin users once in a while

While the initial 100,000 number may scare developers, the final 50 ms is far less scary. We won't calculate the amount of time we need to loop through all that items, as this is a far less significant number.

Unfortunately, this kind of banal analysis wasn't made, and the complex solution was implemented. The team faced some serious issues with this solution along the way.

Given the business growth rate and all other factors we have optimised for the situation that could potentially occur in ~200 years. So, increased project's complexity will pay off in ~200 years.

The optimised solution profit is in far imagined future, whereas the unnecessary cognitive load is here with us.

This isn't an "issue" but I just wanted to write a note of support 🎉

A lot of the useful tips and tools mentioned in this document are worth learning and using. OOP, Design Patterns, DDD, small method lengths (side note: never heard anyone say "small classes" -- that one makes no sense to me) are all extremely helpful in the pursuit of easily maintainable/extendable/scalable code... IF they're applied correctly.

I guess the issue is that you might need to apply them incorrectly a few times to learn their benefits.

But as I see it, a general rule of thumb is: Is the thing I'm trying to apply to my code making my FUTURE life harder or easier? When I return to this codebase in six months and have completely forgotten why I made the decisions I did, is the thing I'm trying to apply now actually going to make it easier for me to get back into it?

Or: Is it OK to leave this method long, breaking that recommendation, as long as the method is well-structured, has clearly understandable variables and logic, and has comments to explain what's going on?

You just want to make your future life as easy as possible for yourself (which also makes it easier for your follow team mates).

Rather than trying to be clever, using obscure functions and trying to reduce the number of lines of code, we should be focussing on reducing the number of lines you need to scroll up in order to understand what's going on. That's a much better goal than playing code golf with yourself/your team.

Impress me with how BASIC you've made everything (and, when applied correctly, the recommendations mentioned in this article with HELP with that).