KISS in Programming: Introduction

When I started writing software, people always tell me about patterns, architectures, clean code, solid, layered design, all those big words. They are all helpful, of course. But after many years of building systems in real companies, fixing production issues at 3 AM, rewriting parts of old code, and even working on payment systems with a lot of compliance work, I realized something that I did not appreciate before.

KISS.

Keep It Simple Stupid.

Some developers do not like how it sounds, but the meaning behind it really helps in real projects. KISS is one of the most practical principles I ever learned because simplicity is not only for beginners. It is for everyone who wants to avoid unnecessary pain.

In this introduction article, I want to share what KISS means to me, how it helped me in real projects, and some simple examples from my own code. I am not writing this like a textbook. I want to explain it like how I would tell it to a teammate while drinking coffee in the pantry.

Let us get started.

What KISS Really Means in Real Projects

Many people think KISS means you should write childish code or avoid architecture. That is not true. What it really means is to avoid making things more complicated than they need to be.

When a requirement is simple, keep the solution simple too. Do not over engineer. Do not build a rocket ship when all you need is a tricycle.

In one of my projects, we needed to generate a daily report for our internal team. The first instinct of the team was to build a reporting microservice, add caching, background workers, an entire ETL flow, and even containerization. Sounds nice. But the business just needed a CSV file emailed daily. The data was very small. No need for a separate database, no need for a background queue, no need for a separate service.

I wrote a small C Sharp console app that queries the database, transforms the result, and emails the CSV. Done in two hours. Running in production for months with no issue. That is the KISS principle working quietly in real life.

Why We Tend To Overcomplicate

There are many reasons why developers complicate things, even unknowingly.

• We want to follow the latest architecture trend.
• We think future requirements will need something bigger.
• We want our code to look smart.
• We do not want teammates to think we wrote low level code.

But after many real world projects, I learned something important. Simple code is not low quality. Simple code is actually harder to write because you need to think clearly. Complicated code is easy to generate, especially when you start imagining problems that are not existing yet.

KISS forces you to think about what you really need.

A Real Story: When Simpler Architecture Was Actually Better

I worked on a system where we tried to apply a very strict clean architecture design. Layers everywhere. Every dependency inverted. Every small object injected. It looks clean on the diagram. But on actual development, new developers struggled. Each small change required editing files in three or four folders. Sometimes I even forgot which layer should call which layer.

The goal of clean architecture is separation and maintainability. It is good. I am not against it. But for that specific project, the complexity was higher than the actual business requirement.

In another project, I used a much simpler architecture. Controllers, services, repositories. That is all. No too deep abstraction. No super generic patterns. And funny enough, this simpler structure handled bigger traffic with less confusion. Our junior devs onboarded faster. Our QA team understood the flow easier. And adding new features was quicker.

KISS is not against clean architecture. But KISS reminds us that architecture must serve the project need, not the ego of the developer.

Simple Code is Easier To Debug

If you have spent nights trying to debug a complicated code, you know what I mean. I remember one bug in an old project where a simple filtering logic was wrapped in layers of abstraction. Something like this:

public interface IFilterStrategy<T>  
{
    IQueryable<T> Apply(IQueryable<T> query);
}

public interface IFilterStrategy<T>  
{
    IQueryable<T> Apply(IQueryable<T> query);
}

Then different strategy classes applied different filters. Sounds smart, right? But all we needed was this:

var result = items.Where(x => x.Status == "Active");

var result = items.Where(x => x.Status == "Active");

That is it.

When the logic broke, we had to trace the filter strategies one by one. But with a simple where condition, it is clear and obvious.

KISS does not mean no patterns. But do not use patterns if you do not need them.

A Simple Example: Filtering and Querying Data

Here is a simple Linq example from a real business requirement. The request was only to display active users with optional filtering.

Instead of creating a fully dynamic query builder with expression trees, I just did this:

var query = _db.Users.Where(x => x.IsDeleted == false);

if (!string.IsNullOrEmpty(request.Role))
{
    query = query.Where(x => x.Role == request.Role);
}

if (!string.IsNullOrEmpty(request.Search))
{
    query = query.Where(x => x.Name.Contains(request.Search));
}

var results = query.ToList();

var query = _db.Users.Where(x => x.IsDeleted == false);

if (!string.IsNullOrEmpty(request.Role))
{
    query = query.Where(x => x.Role == request.Role);
}

if (!string.IsNullOrEmpty(request.Search))
{
    query = query.Where(x => x.Name.Contains(request.Search));
}

var results = query.ToList();

Simple, readable, maintainable.

Later, when we needed to add pagination, it blended naturally.

var results = query
    .Skip((page - 1) * pageSize)
    .Take(pageSize)
    .ToList();

var results = query
    .Skip((page - 1) * pageSize)
    .Take(pageSize)
    .ToList();

Nothing fancy, but easy to understand even for a new developer.

Joining Tables Without Overcomplicating

In one of my APIs, we needed to show orders together with the customer name. Some devs wanted to create a domain model that wraps the join result with mapping layers and DTO factories.

I just wrote the query directly.

var orders = 
    from o in _db.Orders
    join c in _db.Customers on o.CustomerId equals c.Id
    select new OrderDto 
    {
        OrderId = o.Id,
        CustomerName = c.FullName,
        Amount = o.Amount,
        Created = o.CreatedAt
    };

var list = orders.ToList();

var orders = 
    from o in _db.Orders
    join c in _db.Customers on o.CustomerId equals c.Id
    select new OrderDto 
    {
        OrderId = o.Id,
        CustomerName = c.FullName,
        Amount = o.Amount,
        Created = o.CreatedAt
    };

var list = orders.ToList();

Sometimes, the simplest join is the best join.

When KISS Helps With Business Requirements

KISS is not only for code. It also helps in solving business problems.

When the business team requested to support sending notifications, the idea of building a full notification microservice was tempting. Queueing, persistence, retries, templates, all of that.

But what they needed at that moment was only to notify the internal team when something fails.

Solution? I used a simple email sender service inside the main app. No external service. No queue. No database. Just a straightforward function.

await _email.SendAsync("admin@example.com", subject, body);

await _email.SendAsync("admin@example.com", subject, body);

Later, when the need became bigger, that is when we separated it. But at the start, KISS avoided unnecessary work.

KISS vs Future Proofing

Some developers argue that simple solutions are not future proof. But future proof does not mean you must build everything today. Sometimes future proof means you write clean and modular enough code so that you can upgrade later if needed.

KISS encourages you to design simple but not messy solutions.

I learned that preparing for imaginary future requirements wastes time. Shipping a working feature now and refactoring later when real requirements come in is more effective.

When KISS Might Not Work

To be honest, KISS is not the answer to everything. When you work in a project where strict security, compliance, or performance matters, sometimes simplicity is not enough. For example, in payment systems, you must follow standards even if the code becomes more layered.

But even in those complex systems, you can still apply KISS inside each module by avoiding unnecessary abstractions.

A Practical Checklist I Use

Here are the questions I ask myself before coding anything.

1. What is the simplest way to solve this requirement today
2. Is there a reason this simple solution will break soon
3. Is a more complex design actually needed or just nice to have
4. Can a junior developer understand this code
5. If something fails in production, will I understand this code immediately

If I answer yes to everything, then I know I applied KISS properly.

Final Thoughts

KISS in programming is not about writing naive code. It is about writing code that solves real problems without unnecessary complexity. Over the years, I realized that simple code survives longer, is easier to maintain, and makes the development team faster and happier.

In the next parts of this series, I will go deeper into how to apply KISS in architecture, in database design, in API development, and even in debugging.

I hope this introduction helps you appreciate KISS the same way it helped me through many projects.