Software that works today isn’t necessarily software that can survive tomorrow.
One of the easiest things to say after shipping a feature is:
“It works.”
The API returns the expected response. The UI behaves correctly. The database stores the data. The tests pass. The feature is deployed to production.
So, is the job done?
Not necessarily.
There is a significant difference between making software work and making software maintainable. And I think this difference becomes more obvious the longer you work with large, aging codebases.
Making Software Work
When we’re building a feature, our first goal is usually simple: make the requirement work.
Requirement → Implementation → Testing → Works → Done
There’s nothing wrong with that software needs to work before anything else matters. But the problem starts when we treat “working” as the final definition of quality.
A feature can work perfectly today and still become a problem six months later. Maybe the implementation is tightly coupled to other parts of the system. Maybe the business logic is duplicated in several places. Maybe nobody remembers why a particular condition exists, or changing one function unexpectedly breaks three other features.
The software works. But changing it is painful.
Maintainability Changes the Question
When thinking about maintainability, the question changes. Instead of only asking “Does this feature work?”, we start asking “How difficult will it be to change this feature in the future?”
That shifts the entire process:
Requirement → Design → Implementation → Testing → Deployment → Monitoring → Future Changes
Because software is rarely static. Requirements change, users change, business rules change. Developers leave and new ones join. Traffic increases, infrastructure changes, frameworks get upgraded and the software has to evolve with all of it.
The Real Cost of Software Is Often the Cost of Change
One thing I’ve started to appreciate is that maintainability is strongly related to the cost of change.
Imagine two systems.
System A — you need to add a new feature. You modify two files, update a few tests, and deploy: 2 files changed, 1 test updated, 10 minutes.
System B — the same feature requires changes across twelve files. You discover duplicated business logic, then an unexpected database dependency. One change breaks another feature, and you spend an hour debugging something unrelated to the original requirement: 12 files changed, 3 unexpected side effects, 1 regression, 2 hours debugging.
Both systems can technically “work.” But System A is more maintainable.
This is why I don’t think maintainability is simply about having “clean code.” A better question is: how expensive is it to safely change this system?
Technical Debt
Technical debt is often described as bad code. I think it’s more useful to think of it as a trade off.
Sometimes you intentionally choose a simpler or faster implementation because of a deadline that’s not necessarily a mistake. The problem happens when the temporary solution becomes permanent:
Quick implementation → Ship faster → Technical debt → More coupling → Harder changes → More bugs → Slower development
Eventually, the cost of changing the software becomes larger than the time saved initially. That’s the interest we pay on technical debt.
The dangerous part is that technical debt usually doesn’t hurt immediately. The code works, the feature ships, everyone is happy until someone needs to change it.
Technical Debt Isn’t Always Bad
Sometimes taking on technical debt is a reasonable engineering decision. Maybe you’re validating a new product idea and don’t yet know whether the feature will even be used. Building a highly abstract architecture before validating the requirement could be wasteful a simple implementation might be the right call.
The important part is knowing you’re making a trade off. The problem is when we forget the debt exists, the temporary solution becomes permanent, and months later someone asks: “Why is this implemented like this?” and nobody remembers anymore.
Maintainability Is Bigger Than Clean Code
When people talk about maintainability, the conversation often goes straight to clean code: readable variables, small functions, SOLID principles, design patterns. Those things help, but maintainability is much bigger than code formatting.
Code should be reasonably readable, predictable, testable, and loosely coupled.
Database design affects maintainability too. A poorly normalized schema, a missing index that only becomes a problem at scale, or a query that silently N+1s as data grows these turn a simple feature request into a multi day migration project.
Architecture needs understandable boundaries. Each component should have a clear responsibility; if everything depends on everything else, every change becomes risky.
Testing isn’t only about proving the software works it gives engineers the confidence to say “I changed this part, and the existing behavior still works.”
Deployment is part of maintainability too. If shipping a change requires ten manual steps that only one person knows, the system isn’t operationally maintainable.
Observability — logs, metrics, monitoring, error tracking is what lets engineers understand why something broke, not just that it broke. A production incident with no logs to trace often turns a 10 minute fix into a multi hour investigation.
Documentation matters most for business rules and architectural decisions. Not every line needs a comment, but “why does the system behave this way” should be discoverable somewhere.
The Next Developer Test
A simple test I like to think about:
If another engineer had to modify this code six months from now, would they understand it?
Imagine the original developer is no longer available. Can someone else understand the architecture, find where the business logic lives, understand why certain decisions were made, run the project locally, safely modify the feature, and deploy the change?
If the answer is no, there’s probably a maintainability problem. This doesn’t mean every system needs massive documentation it means the system shouldn’t depend entirely on the memory of the person who wrote it.
Abstraction Can Also Become a Problem
There’s another trap: over engineering. Sometimes we think maintainable software means adding more abstraction — so instead of Controller → Service → Repository, we end up with Controller → Service → Manager → Handler → Processor → Repository → Provider, all to perform a relatively simple operation.
More abstraction doesn’t automatically mean more maintainability. Sometimes it just creates more cognitive load — the engineer now has to jump through seven layers to understand what the application is doing.
Good abstraction removes complexity. Bad abstraction hides complexity.
There’s also a tendency to associate complexity with technical maturity. A complicated architecture can look impressive, but complexity has a maintenance cost. Sometimes the best solution is simply a clear function, clear naming, a simple query, a good test, and good documentation. There’s nothing wrong with boring software — it’s often easier to operate and maintain.
Worth noting: using a modern framework doesn’t automatically make software maintainable either. You can build an unmaintainable system in Laravel, React, Next.js, Spring, Go, or Rust just as easily as in anything else. Frameworks are tools — they provide a foundation, but they don’t make architectural decisions for you. How you use the technology matters more than which technology you chose.
Working With Legacy Code
This becomes especially obvious when working with an existing codebase. When building something new, everything feels clean — you know why the architecture looks the way it does, why a table exists, why a workaround was needed.
But when you inherit an old system, you don’t have that context. You might see a chain of if / else if / else if and think “why didn’t they just do this differently?”
Code usually has a history. Maybe there was a business requirement, a production incident, a tight deadline, or a limitation in the original system that no longer applies. This is why maintaining software requires understanding the past, not just implementing the future.
Before rewriting legacy code, the more valuable question often isn’t “how can I rewrite this?” it’s “why was this built this way in the first place?”
Refactoring Is Not Just “Cleaning Up”
Refactoring is often treated as something we do when we have free time “we’ll clean this up later.” But later rarely comes on its own. If technical debt keeps accumulating, the system gradually becomes harder to change, and harder to change systems leave even less time for refactoring. It’s a cycle that feeds itself.
This doesn’t mean every piece of code needs immediate refactoring. Refactoring should have a purpose: reducing duplication, simplifying a complex module, making future changes safer, improving testability, reducing coupling. The goal isn’t to make the code beautiful it’s to make future change cheaper and safer.
Don’t Optimize for Today’s Developer
One mindset I think is important: don’t optimize software only for the developer who wrote it optimize it for the developer who will maintain it.
That means not relying on tribal knowledge, not hiding important business logic, not creating unnecessary complexity, and not making deployment dependent on one person. The code isn’t your personal property. Once it’s part of a production system, it belongs to the team and the business.
A Practical Checklist Before Calling It “Done”
Before merging, it’s worth running through a few quick questions:
- Code — Could someone else understand this without asking me?
- Architecture — Can this change without breaking something unrelated?
- Database — Are the queries and schema going to hold up as data grows?
- Testing — Would we catch a regression here?
- Deployment — Can anyone on the team ship this, not just me?
- Operations — If this breaks at 2am, can whoever’s on call figure out why?
None of these guarantee perfect software. But they push the question past “does it work?” toward “will this still be easy to work with in six months?”
Final Thoughts
I used to think the main goal of software engineering was simply to build features that work. Now I think that’s only the beginning.
The harder problem is building software that can continue to evolve because software doesn’t stay the same. Requirements change, teams change, technology changes, businesses change, and the code needs to survive all of it.
That’s the meaningful difference between making software work and making software maintainable.
Working software solves today’s problem. Maintainable software makes tomorrow’s problem easier to solve.
Software isn’t done when it works. It’s done when it’s still safe to change.
