Software rarely becomes difficult to maintain because it is old. It becomes difficult when complexity accumulates faster than the architecture can handle it. Here is what actually makes a codebase expensive to change — and how experienced engineers prevent it.
Eng Abdalla Ali
8 min read
A software system rarely becomes difficult to maintain overnight.
It usually happens gradually.
A small feature is added here. A quick workaround is introduced there. A database table gets another field. An API starts doing one more thing. A component becomes responsible for several unrelated behaviors.
At first, nothing seems wrong.
The application works.
Users are happy.
The team keeps shipping.
Then, months or years later, a developer is asked to make what should be a simple change.
Instead of changing one file, they discover twelve dependencies.
A seemingly harmless modification breaks another feature.
A database change requires updates across multiple services.
Nobody is completely sure why a particular piece of code exists.
This is how software becomes expensive to change.
The real problem is not that the codebase became large. The problem is that complexity started growing faster than the system's structure could contain it.
Software Complexity Is Not the Same as Software Size
Large systems are not automatically bad systems.
Some of the world's most reliable software contains millions of lines of code.
The important distinction is between size and complexity.
The authentication system does not need to know exactly which email provider is being used.
Written by
Eng Abdalla Ali
Senior Software Engineer & Co-Founder
Co-founder focused on system architecture and product design — shaping scalable platforms and intuitive experiences for institutions across East Africa.
The problem is that the system has stopped expressing its domain boundaries clearly.
A mature codebase should make it reasonably obvious where a piece of behavior belongs.
Instead of asking:
"Where can I put this?"
a developer should be able to ask:
"Which part of the system owns this responsibility?"
That difference matters.
The Third Problem: Technical Debt
Technical debt is often misunderstood.
It does not simply mean "bad code."
Sometimes technical debt is a deliberate trade-off.
A startup may choose a simpler implementation because shipping the product quickly is more valuable than building a highly abstract architecture on day one.
That can be reasonable.
The problem begins when temporary decisions become permanent architecture.
Quick Solution
↓
Ship Feature
↓
Business Changes
↓
More Features
↓
Workaround
↓
Another Workaround
↓
Complexity
Eventually, developers spend more time working around the original shortcut than they would have spent designing the system properly.
Technical debt behaves much like financial debt.
A small amount can be manageable.
Ignored debt accumulates interest.
The Fourth Problem: Abstractions That Arrived Too Early
Experienced engineers often talk about avoiding premature abstraction.
There is a reason.
Imagine two features that currently look similar:
Feature A
Feature B
A developer might immediately create:
GenericFeatureEngine
with dozens of configuration options.
Six months later, the two features evolve differently.
The abstraction now has to support contradictory requirements.
Instead of simplifying the system, it has become another source of complexity.
A useful rule is:
Do not abstract because two things look similar. Abstract when they have a stable reason to change together.
Duplication is sometimes cheaper than the wrong abstraction.
The Fifth Problem: Business Logic Everywhere
Business rules should have a clear home.
When they are scattered across controllers, UI components, database queries, background jobs, and utility functions, the system becomes difficult to reason about.
Now the same business rules exist in multiple places.
Eventually, one implementation changes while another does not.
A better approach is to centralize important domain rules:
User Interface
↓
Application Service
↓
Domain Rules
↓
Infrastructure
The exact architecture can vary.
The principle remains the same:
Important business decisions should not be scattered across the system.
The Database Can Become the Hidden Source of Complexity
Developers often focus heavily on application code while underestimating database design.
That can become expensive.
Poorly designed schemas create problems that appear everywhere else.
Examples include:
Duplicated data
Unclear relationships
Inconsistent naming
Missing constraints
Inefficient queries
Tables that represent multiple unrelated concepts
Business rules hidden inside application code
A database is not simply storage.
It is part of the architecture.
A good schema can enforce important invariants and make the rest of the application simpler.
A poor schema forces application code to compensate for structural problems.
The API Is an Architectural Boundary
APIs can also become difficult to maintain when they are designed around implementation details rather than domain concepts.
For example:
POST /updateUser
POST /doSomething
POST /processData
may work initially.
But as the application grows, unclear APIs make it difficult for developers to understand what the system actually guarantees.
A well-designed API communicates intent.
It establishes:
What operation is being performed
What data is accepted
What the caller is allowed to do
What happens when something fails
What the response represents
The API becomes a contract between different parts of the system.
Contracts should be stable and intentional.
The Cost of "Just One More Condition"
Some of the worst complexity starts with innocent-looking conditions.
if user.isAdmin
...
if user.isAdmin && user.isVerified
...
if user.isAdmin && user.isVerified && user.hasSubscription
...
Eventually, business logic becomes a collection of conditions that nobody wants to touch.
This is often a sign that the domain needs better modeling.
Instead of continuously adding conditions, the system may need explicit concepts such as:
Role
Permission
Subscription
Organization
Feature Access
Account State
Good domain models replace mysterious conditions with understandable concepts.
Why Small Changes Become Dangerous
One of the clearest signs of architectural problems is the size of the change compared with the size of the requirement.
The requirement:
Add a new field to a user profile.
The implementation:
Database migration
↓
ORM model
↓
API schema
↓
Validation
↓
Service
↓
Controller
↓
State management
↓
Three UI components
↓
Tests
↓
Background synchronization
Some of this is normal.
But if every small change requires touching a large portion of the system, the architecture may have excessive coupling.
A healthy system should make common changes relatively local.
Not every change can be local.
But many should be.
Tests Are Architectural Feedback
Tests are usually discussed as a way to prevent bugs.
They are also useful architectural feedback.
If testing one business rule requires starting the entire application, connecting to multiple external services, and preparing a large database state, the system may have poor boundaries.
Compare that with:
Business Rule
↓
Pure Service
↓
Unit Test
The second system is easier to understand and easier to change.
Testability is therefore not just a testing concern.
It is often evidence of good separation.
Observability Matters as Systems Grow
Maintenance is not only about changing code.
It is also about understanding what the system is doing in production.
As systems grow, engineers need answers to questions such as:
Where did this request fail?
Which service caused the problem?
How long did the database query take?
Which deployment introduced the regression?
How many users were affected?
Logging, metrics, tracing, and error monitoring become increasingly valuable.
Without observability, engineers are forced to guess.
And guessing becomes expensive at scale.
Documentation Should Explain Decisions
Documentation does not need to describe every line of code.
Good documentation explains things that are difficult to discover from the code itself.
For example:
Why is this service separated from the API?
Why does this database table use this structure?
Why was this technology selected?
What constraints does this system have?
These are architectural decisions.
A short explanation today can save hours of investigation later.
The Best Architecture Is Not the Most Complicated One
There is another trap.
Once engineers learn architecture principles, it is tempting to use all of them.
A small application does not need the architecture of a global platform.
The right architecture is proportional to the problem.
Good engineering is not about maximizing architectural sophistication.
It is about minimizing unnecessary complexity while preserving the ability to evolve.
Refactoring Is Part of Engineering
Refactoring is sometimes treated as work that should happen only when there is spare time.
In healthy engineering teams, refactoring is part of maintaining the system.
That does not mean rewriting everything.
A good refactoring often looks small:
Before
──────
Large Service
↓
Many Responsibilities
After
─────
User Service
Payment Service
Notification Service
The goal is not to make the code beautiful for its own sake.
The goal is to make future changes safer and cheaper.
When Should You Rewrite?
A rewrite is one of the most expensive decisions a team can make.
Sometimes it is justified.
Often it is not.
A system may be painful because of a few architectural boundaries rather than because the entire codebase is fundamentally broken.
Before rewriting, ask:
Which parts are actually causing the problem?
Can they be isolated?
Can the architecture be improved incrementally?
What knowledge would be lost in a rewrite?
Can the existing system continue serving users during the transition?
A targeted refactor is often safer than starting from zero.
Designing for Change
The best software architecture is not architecture that never changes.
It is architecture that can change without collapsing.
Requirements will change.
Customers will change.
Business models will change.
Technologies will change.
Teams will change.
A good system expects this.
That means creating boundaries around areas likely to evolve.
It is to avoid making today's decisions unnecessarily expensive tomorrow.
A Maintainable System
A maintainable system does not have to be perfect.
It needs to be understandable.
A developer joining the project should be able to answer:
Where does this feature belong?
Who owns this business rule?
Which service controls this data?
What happens when this operation fails?
Which component is safe to change?
How do I test this behavior?
If the answers are clear, the system is probably in good shape.
If every answer begins with:
"It depends, but let me show you..."
the architecture may already be carrying too much accidental complexity.
Conclusion
Software systems become difficult to maintain when complexity accumulates without clear boundaries.
The biggest problems are rarely caused by one terrible decision.
They usually emerge from dozens of small decisions:
A shortcut that became permanent.
A dependency that spread too far.
A business rule copied into three places.
An abstraction introduced before its shape was understood.
A database schema designed around today's requirements.
A feature added without considering how the system will evolve.
None of these decisions necessarily breaks the application immediately.
Their cost appears later.
That is why software engineering is not simply about making software work.
It is about making software continue to work while the world around it changes.
The best codebase is not the one with the fewest lines.
It is not the one with the most sophisticated architecture.
It is the one where engineers can understand the system, make changes safely, and keep moving without fighting the architecture at every step.
Good software is built for today's requirements.
Great software is structured for tomorrow's changes.
