Why software problems are not code problems
Most software systems don’t fail. They survive.
They keep running, keep serving users, keep appearing “stable” — while quietly becoming more expensive, more fragile, and more feared every year.
Teams complain about legacy code, bad architecture, and messy systems, but rarely ask the harder question:
Why do we keep building software that works, yet nobody dares to change?
The answer is uncomfortable, because it has less to do with frameworks or patterns, and much more to do with how we think while building software in the first place.
When We Talk About Software Problems
When we talk about software problems, we usually talk about code.
- Messy code
- Legacy code
- Bad architecture
- Systems nobody dares to touch
We complain about:
- projects that “work” but cost a lot to run
- systems that are slow to change
- services that survive for years without improving
- teams that keep adding code but never make things better
These problems are everywhere.
And the question we rarely ask is not how to fix them, but:
Why do we keep creating systems like this in the first place?
A Real Story From a Real System (Story #1)
There was a system built more than ten years ago.
At the time:
- the company owned its own data center
- traffic was small
- database access was cheap and unlimited
- the team was small and under pressure to deliver fast
So the system grew naturally — but without structure.
To move faster:
- logic went into one file
- database queries were written freely
- business rules, control flow, and data access mixed together
Over time:
- that one file became over 8,000 lines
- nested conditions were everywhere
- class names only the original author understood
And still — it worked.
Years later, the company moved to AWS.
The database became Aurora.
Every query now had a price.
Nothing in the business logic changed.
But suddenly, the system started showing up on the monthly bill.
Hundreds of dollars.
Then thousands.
Everyone knew:
“We could reduce this cost a lot.”
But nobody touched the code.
It wasn’t because the team didn’t know SQL.
It wasn’t because they didn’t know optimization.
It was because the system was too risky to change.
The system survived — not because it was good,
but because it was hard to kill.
Another Real Story (Story #2)
I’ve also seen a different kind of system.
It looked “clean” on the surface.
There were:
- controllers
- services
- repositories
- interfaces
Each folder was organized.
Each layer had its place.
But when a new feature was requested, something strange happened.
Developers spent days:
- tracing logic across files
- jumping between layers
- trying to understand where the real business rules lived
Nothing was obviously wrong.
But nothing was obviously clear either.
To add a feature, the easiest solution was always:
“Let’s just add another service file.”
Over time:
- the number of files grew
- duplication increased
- behavior became implicit, not explicit
Again, the system worked.
Again, nobody dared to refactor deeply.
So we have to ask:
Why do systems that “work” so often become systems we fear?
The Easy Answers (And Why They Are Wrong)
People usually give simple explanations:
- “They didn’t know design patterns.”
- “They didn’t follow clean architecture.”
- “They didn’t know SOLID principles.”
- “They didn’t write tests.”
These answers are comfortable — and mostly wrong.
I’ve seen teams that:
- knew SOLID
- knew clean architecture
- knew patterns
- organized folders correctly
And still produced systems that were hard to change.
So the problem is not knowledge.
And it’s not a missing pattern.
The Real Root Cause: The Software Engineering Mind
At some point, I realized something important:
- Architecture does not come first.
- Design principles do not come first.
- The engineering mind comes first.
Good or messy systems are not created by accident.
They are created by how people think while building them.
Two developers can face:
- the same requirements
- the same deadlines
- the same pressure
One creates something that evolves.
The other creates something that freezes.
The difference is not skill.
It’s not experience.
It’s not tools.
It’s the mind behind the decisions.
Software Is Invisible — That Changes Everything
Unlike hardware, software is invisible.
You can’t:
- touch it
- weigh it
- feel its quality immediately
A piece of software can:
- run correctly
- pass tests
- serve users
And still:
- cost too much to operate
- require too many people to maintain
- slow down every future change
The real cost appears late:
- in cloud bills
- in on-call pain
- in slow delivery
- in burned-out teams
Because the damage is invisible,
the engineering mind matters more than the code itself.
What Is the Software Engineering Mind? (In Practice)
This is not about being perfect.
It’s not about knowing all the answers.
From my experience, the software engineering mind shows up in a few very concrete ways.
1. Thinking Beyond “It Works”
A developer stops at:
“It works.”
An engineer keeps going:
- How will this run at scale?
- How will this change?
- What will this cost — not just today, but later?
This is why some systems quietly become expensive,
while others stay manageable.
2. Treating Software as a Craft, Not a Task
With this mind:
- shortcuts are taken consciously, not blindly
- clarity is valued even when nobody asks for it
- responsibility extends beyond the ticket
Like a craftsman:
- you know shortcuts exist
- but you also know what they cost
This mindset doesn’t slow teams down.
It prevents teams from getting stuck.
3. Respecting Invisibility
Engineers with this mind understand:
- complexity hides
- problems accumulate quietly
- systems don’t fail immediately — they decay
So they design with humility.
They expect change.
They leave room for replacement.
This is why their systems are easier to refactor — or shut down.
4. Seeking Problems, Not Just Solving Tasks
Some people solve exactly what is asked.
Others ask:
- “What will hurt later?”
- “What are we afraid to touch?”
- “Why is this part so fragile?”
Legacy systems are rarely created by one bad decision.
They are created when no one looks for problems early.
Rethinking “Good” and “Bad” Code
I don’t like labeling code as “good” or “bad”.
That’s too simple.
A better distinction is this:
- Some code invites change
- Some code resists change
Good systems are not immortal.
They are easy to replace.
Bad systems don’t survive because they are strong.
They survive because they cannot be changed safely.
This is the paradox:
The hardest systems to kill are often the worst ones.
From Developer to Engineer
Becoming a great engineer is not about memorizing patterns.
It’s not about copying architectures from books.
It’s about developing a mind that:
- sees beyond today’s task
- respects long-term cost
- treats software as a living system
- owns consequences, not just output
With this mind:
- even imperfect designs improve over time
- even legacy systems become better
- even unknown problems find solutions
Without it:
- no framework saves you
- no architecture survives
- no amount of knowledge prevents decay
Final Thought
Good software does not come from knowing the right answers.
It comes from having the mind that knows:
- how to ask better questions
- how to find solutions when none are obvious
- how to build things that can change — or disappear — when needed
That is the difference between writing code
and practicing software engineering.
And that mind is what truly separates
a good developer from a great engineer.
Top comments (0)