Code Maintainability: How to Make Changes Safer

A codebase rarely becomes difficult to maintain overnight. The problem usually grows gradually: a responsibility becomes poorly defined, an unnecessary dependency is introduced into the project, a test stops representing the system’s actual behavior, and a temporary solution ends up becoming permanent.

Individually, these decisions seem small. The cost becomes visible when a simple change starts requiring hours of investigation because no one knows for sure what might break.

That is how I think about code maintainability: the ability to understand, change, test, and review software without turning every task into a reconstruction of the entire system.

When maintainability declines, the team still ships. The difference is that every change starts requiring more context, more review, and more caution. Engineers spend more time understanding the code than improving the product, PRs move more slowly, and small changes begin causing unexpected side effects.

In this article, I will show you how to recognize these signs, what makes code easier to maintain, and how to use tests, documentation, code review, and automation to protect the codebase as the product continues to evolve.

What is code maintainability?

Code maintainability is the ease with which software can be understood, fixed, tested, and modified over time.

To me, maintainable code provides enough context for the next person to make a change without having to guess how the system works.

Imagine a pull request that changes how an invoice is calculated. In a healthy codebase, the reviewer can locate the rule, understand which tests protect that behavior, identify the dependencies involved, and assess whether the implementation follows the current design.

The change may still require careful attention, but the work needed to understand it is visible.

In a difficult-to-maintain codebase, the same rule may be spread across multiple files, depend on implicit behavior, and have tests that cover only part of the flow. In that case, the review stops being an analysis of the change and becomes an investigation into everything around it.

The ISO/IEC 25010 model describes maintainability through characteristics such as modularity, analyzability, modifiability, reusability, and testability. In day-to-day work, I would translate that into more direct questions:

• Can I change a rule without modifying several parts of the system?
• Can I quickly understand what this code does?
• Is the impact of the change clear?
• Do the tests actually protect the behavior being changed?
• Would someone else be able to modify this code safely later?

When the answers to these questions depend on someone’s memory, the code is already carrying more risk than it should.

Why maintainability gets worse over time

Most maintainability problems do not come from a single bad decision. Maintainability deteriorates through the accumulation of compromises made during product development.

A deadline gets tighter, a temporary solution is added to the code, a refactoring is postponed, and new behavior is implemented on top of a structure that was already reaching its limit. Each decision may have made sense at the time. The problem is that few of them are revisited later.

Over time, the codebase starts showing patterns such as:

• business rules spread across multiple files;
• modules that depend on the internal details of other modules;
• names that describe technical operations but hide the product behavior;
• tests that execute the code but fail to validate the most important scenarios;
• documentation focused on configuration without explaining architectural decisions;
• PRs approved because nothing is clearly broken, even when the design is becoming harder to evolve.

That is why I would not treat maintainability as a cleanup task performed once or twice a year. The cheapest time to protect it is while the code is being changed and the context is still fresh for the people developing and reviewing it.

What makes code easy to maintain

Maintainable code does not need to be “sophisticated.” In most cases, it needs to be predictable, clear, and consistent with the way the rest of the system was built.

Names that explain the domain

A good name reduces the time required to understand the code.

processData() says very little about what is happening. The reader still needs to open the function, follow its calls, and determine what kind of data is being processed.

Names such as calculateInvoiceTotal() or applyRegionalTaxRules(), on the other hand, make the intent clearer before the implementation is even read.

This makes a difference, especially during code review. When names hide behavior, the reviewer has to reconstruct the flow, open related files, and remember how that part of the system works. The review becomes slower because the code does not explain enough on its own.

Components with clear responsibilities

Code becomes easier to change when each module has a clearly defined role.

That does not mean splitting everything into tiny functions and classes. Excessive fragmentation can also make the system harder to understand. The goal is to organize the system so that the team knows where each decision belongs.

When a product rule changes, it should be possible to locate its implementation without searching through several different layers. Once it is found, the impact of changing it should also be clear.

Tests that protect behavior

A good test suite does more than confirm that the code runs. It shows which behaviors the system needs to preserve.

The most useful tests for maintainability cover business rules, edge cases, permissions, failures, data limits, and scenarios in which an error would have a real impact.

This makes refactoring safer. Instead of keeping a poor structure out of fear that something unknown might break, the team can improve the code and use the tests to verify that the behavior remains correct.

Coverage helps, but it should not be interpreted in isolation. A codebase can have 90% coverage and still leave its most critical flows untested. During review, I would focus less on the percentage alone and more on what the tests actually guarantee.

Documentation that explains decisions

In my opinion, the most valuable documentation records what the code cannot explain on its own.

Comments that merely repeat the implementation tend to create noise. A short note explaining why records must be processed in a specific order, on the other hand, can prevent hours of investigation in the future.

The same applies to READMEs, architecture decision records, and pull request descriptions. The question I would ask is simple: what does someone need to know to change this code six months from now without repeating a mistake the team has already solved?

How maintainability degrades in pull requests

A pull request is one of the points where maintainability can either be protected or weakened.

Passing CI does not necessarily mean that a change is easy to maintain. The linter may be green, the tests may pass, and the behavior may work, but the PR can still place a rule in the wrong module, create an unnecessary dependency, or introduce an abstraction that differs from those already used in the project.

That is why, during review, I would try to answer:

• Is the rule located where the team would expect to find it?
• Can someone else understand the change without relying on verbal context?
• Does the PR introduce a dependency that could make future changes more difficult?
• Would the tests fail if the important behavior were broken?
• Does the description explain the implementation trade-offs?
• Is there a risk that this pattern will be copied elsewhere in the system?

That last question is especially important. A confusing helper or a rushed abstraction can spread quickly because other developers begin using it as a reference.

Code review does not influence only the code being merged today. It also helps define which patterns will be repeated tomorrow.

How code review protects maintainability

A healthy code review process finds more than bugs. It helps the team identify problems that automated tools cannot always assess.

A reviewer can identify when:

• a module has become harder to understand;
• a name does not accurately represent the behavior;
• edge cases are missing;
• a rule has been placed in the wrong layer;
• a shortcut should become a follow-up task;
• a temporary solution is at risk of becoming permanent.

The challenge is maintaining this level of quality consistently.

Senior engineers may recognize these risks, but they do not always have time to review everything with the same depth. Less experienced engineers may notice that something is wrong but struggle to explain the problem clearly. And when each team adopts different criteria, the same patterns end up being discussed repeatedly across multiple PRs.

That is why I like turning recurring decisions into explicit rules.

If the domain layer should not import infrastructure, document that rule. If every behavior change requires tests, make that criterion visible. If certain files are growing too large, flag them before the problem becomes normal.

Maintainability improves when the team stops relying solely on the reviewer’s memory.

Tools that help maintain code quality

No single tool can solve maintainability on its own. Each one can identify a different type of problem.

• Formatters prevent recurring discussions about style.
• Linters enforce conventions and find simple errors.
• Static analysis identifies known quality, security, and reliability patterns.
• Coverage tools highlight areas that are not sufficiently exercised by tests.
• PR templates help authors explain context, risk, and the validation plan.
• AI code review tools analyze the diff based on repository rules and context.

The most important thing is understanding the limitations of each tool.

A formatter can standardize code, but it cannot determine whether a business rule is in the correct module. A test can prove that a scenario works, but it may not reveal that the design is becoming harder to extend. Static analysis can find known patterns, but many maintainability problems depend on the architecture and the team’s specific conventions.

AI code review can help when it acts as a first filter before the reviewer’s analysis. It can identify duplicated logic, missing tests, risky coupling, and violations of previously defined rules.

The final decision still belongs to the people who understand the product and the system. The tool reduces repetitive work and provides more signals for the review before the merge.

How to improve maintainability without stopping development

Large cleanup projects can be useful, but they are often difficult to prioritize. In practice, the most sustainable improvements happen close to the changes the team is already making.

I would start with the PRs that are already part of the team’s routine:

• ask the author to explain why the change is necessary, not just what was changed;
• review names with the same care used to review logic;
• update tests whenever behavior changes;
• remove duplication when it begins to represent a repeated rule;
• document architectural patterns that frequently appear in review comments;
• track intentional shortcuts as technical debt, with context and an owner;
• refactor the area already being modified instead of waiting for a perfect window.

This approach works because the context is already available. The developer understands the motivation, the reviewer is already reading the files, and the cost of fixing the structure is lower than it will be months later.

That does not mean turning every PR into a major refactoring. Sometimes, the right decision is to ship the change and create a follow-up task. What matters is that the choice is deliberate and that the problem does not disappear simply because the merge happened.

How to measure code maintainability

I would not try to reduce maintainability to a single score. Technical metrics can help identify areas that deserve attention, but they need to be interpreted alongside what happens in PRs and in the team’s day-to-day work.

Technical metrics

Metric	What it helps identify	How I would use it
Cyclomatic complexity	Functions with many execution paths and scenarios that are difficult to test	To find code that is becoming difficult to understand and change
Code duplication	Repeated logic across different parts of the system	To identify business rules that may receive inconsistent fixes
Test coverage	Areas that are not sufficiently exercised by automated tests	To investigate whether critical behaviors are actually protected
Change frequency by file	Files that are changed repeatedly	To locate unstable areas or files that concentrate too many responsibilities
Dependencies between modules	Coupling between different parts of the system	To find changes that may create side effects across multiple areas
Code smells	Long functions, large classes, and structures that are difficult to understand	To track areas that are getting worse over time

These metrics should not block a PR on their own. A more complex function may make sense in a specific context, just as temporary duplication may be better than creating an abstraction too early. I would use the numbers to identify trends and decide where to investigate.

Development workflow metrics

Metric	What it helps identify	How I would use it
Average review time	Areas or types of changes that require more effort to understand	To discover where context is unclear
Number of review rounds before approval	PRs that go back and forth several times before being merged	To find recurring problems with structure, scope, or tests
Repeated comments	Rules that still depend on the reviewer’s memory	To turn recurring patterns into lint rules, tests, documentation, or review rules
Bugs after small changes	Parts of the system with unpredictable side effects	To prioritize tests and refactoring in sensitive areas
Knowledge concentration	Files or modules that only a few people can safely change	To identify risks caused by dependency on specific individuals
Onboarding time by area	How difficult it is for a new team member to make a safe change	To find modules that require too much context outside the code

Combining these two perspectives is usually more useful than relying on any single metric. A module with increasing complexity, limited tests, and PRs that go through several review rounds probably deserves more attention than one with a single static analysis warning.

To me, a metric shows you where to look. Deciding what to improve still depends on the context of the code, the risk of the change, and the cost that area is already creating for the team.

How Kodus helps with this process

Kodus helps engineering teams bring maintainability criteria into the pull request workflow. It analyzes changes using repository context and allows teams to define specific review rules in natural language.

This distinction matters because much of maintainability depends on local context.

A generic rule may identify a long function. A team-specific rule can check whether a domain module is importing the wrong layer, whether a billing change is missing the expected tests, or whether the change violates the organization defined for the monorepo.

Kodus is an open source AI code review platform. Teams can integrate it into their existing review workflow, choose their own LLM, and keep the final decision with the people responsible for the code.

To me, the main advantage is making the team’s standards visible before the merge. The earlier unclear logic, risky coupling, or a missing test is identified, the less work it takes to fix the problem.