How to Write Maintainable Code: From Principles to ROI

Writing maintainable code is an investment decision. Clean-code principles are easy to recite but expensive to enforce. Some practices fall short, and some teams fail to budget for code quality across internal and vendor teams. Picking standards that truly pay off requires a lot of work and hard-won experience.

Key Points:

• Maintainable code is ultimately a budget decision with a measurable ROI.
• Some popular principles tend to cost more to enforce than they save for most teams.
• Code quality is measurable and teams need to know what to look out for.
• The same quality bar can and should be used for internal teams and vendors.

What Maintainable Code Is Really About

Writing clean, maintainable code means writing code that is cheap to modify and easy for a new engineer to understand. That’s the economics of software development.

However, there’s another aspect to consider, from a craftsmanship standpoint: code is elegant when it’s simple and free of unneeded complexity. It is this type of elegance that allows you to make inexpensive changes. Elegant code is simple code. And simple code is code you can easily modify on a Friday afternoon.

CISQ’s 2022 report puts the cost of poor software quality in the US at $2.41 trillion, with accumulated technical debt at $1.52 trillion. At the same time, Stripe’s Developer Coefficient survey found that developers self-report spending 42% of their working time on technical debt and bad code.

These numbers put the leadership question in sharp focus: which coding standards are worth enforcing, and how do you confirm they’re actually being followed?

Let’s first have a look at the common software development principles.

The Principles, and Where They Fail

All engineering managers have heard of SOLID, DRY, KISS, and YAGNI. The bigger challenge is determining which of these principles are worthy of the overhead required to enforce them.

But when does strict adherence to these principles create problems greater than those they solve?

SOLID Principles

The SOLID principles (Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, Dependency Inversion) are typically enforced as if each of the five concepts has equal weight. In practice, two deserve the most enforcement attention.

Single Responsibility Principle (SRP)

SRP is important because when multiple teams touch the same class for different reasons, merge conflicts accumulate, and future modifications become riskier. Let’s note that, while SRP helps prevent a class from having many reasons to change, it can create problems at the service boundary: overly aggressive splits here can result in network calls where function calls could be made (adding latency and failure points).

Dependency Inversion Principle (DIP)

The Dependency Inversion Principle matters because it enables easier testing and lets you replace infrastructure without rewriting business logic. The remaining three principles, the Open/Closed Principle (OCP), the Liskov Substitution Principle, and the Interface Segregation Principle, are mostly applicable to library/API design. Specifically, OCP is often misapplied as a pre-emptive plugin architecture that no one has requested.

Don’t Repeat Yourself (DRY)

The DRY principle (Don’t Repeat Yourself) has its known pitfalls, premature abstraction being the worst. If two pieces of code look identical today but will likely represent separate business domains that diverge tomorrow, the wrong shared abstraction will couple your systems together more tightly than the repetitive code it replaced. A good rule of thumb: when you see the same logic repeated, or similar logic across files, tolerate the duplicate code until the third instance, then abstract.

KISS and YAGNI Principles

Finally, KISS (Keep It Simple, Stupid) and YAGNI (You Ain’t Gonna Need It) serve as opposing forces.

Creating a flexible plugin framework for a feature that receives fewer than 10 user requests per day violates both. No one asked for that level of flexibility. That flexibility has a maintenance cost that compounds with each onboarding and review cycle. And the documentation that would make the framework approachable? Nobody will write it.

KISS and YAGNI also imply knowing when to skip cleanup entirely. For example, early-stage products that have yet to find product/market fit should not invest months optimizing their architecture for features customers don’t want. That is capital misallocated to architecture cleanup that the business may never need. The discipline is to acknowledge delayed cleanup as intentional debt and track it, even when code smells are piling up. Once the product has demonstrated viability, clean it up. Skip tracking and let the debt silently remain forever.

These principles define what to optimize for, but they do not enforce themselves.

The next question is: how does this optimization appear in the code as a new team member reads on their first day?

Readable Code as an Onboarding Investment

Readability practices tie back to one leadership metric: Time-to-First-PR for new hires. A codebase where a new engineer can produce a production-quality pull request in days rather than weeks signals low cognitive load.

Meaningful names have a direct impact on onboarding speed. When descriptive variable names and concise function names follow consistent naming conventions across modules, other developers can navigate the codebase without searching or guessing. Avoid cryptic abbreviations: concise names that communicate intent are preferable. Also, avoid magic numbers.

Function size follows the same idea. Short, reusable functions that perform a single task become self-documenting code: they don’t require comments explaining “what” they do. When multiple developers share a codebase, breaking complex functions into smaller functions makes the code easier to follow.

When you write comments, reserve them for the “why”.

For example, a non-intuitive business rule or workaround for a bug in a third-party library. Consider enforcing the distinction between “what” comments and “why” comments in your review guidelines. In most cases, “what” comments will become irrelevant within months as the code changes, while “why” comments will remain relevant for years. Exceptions exist, though.

The fast inverse-square root in Quake III is famous for its “what” comments (“evil floating point bit level hacking”): it has remained famous for 25 years because it tells every future engineer that this part of the code is deliberately unreadable (”do not ‘fix’ it”).

Readability’s larger concern involves defining module boundaries. Module boundaries should follow business capabilities. Splitting by technology layer (one module for controllers, one for models) scatters related logic across the codebase. Modules such as authentication and logging are true cross-cutting concerns and justify separate modules. Other common functionality should be organized around the domain, as product teams structure their codebase.

Clear naming and short functions are the foundation of writing clean code. Keeping it clean as teams change is the harder problem, because standards drift unless they are enforced.

Enforcement Layers for Clean and Maintainable Code

Teams usually rely on three enforcement layers, each building toward the next. Static analysis catches mechanical problems, while clean commit hygiene gives reviewers the context they need, and code reviews become far more effective when the first two are in place. Reviews are also the most expensive layer.

Static Analysis, Linting, Stylers, and Formatters

Automated formatters (e.g., Prettier, Black for Python) and stylistic linters (ESLint Stylistic for JavaScript/TypeScript) eliminate formatting debates during code reviews.

This kind of automation enables a codebase written by multiple software engineers to be read as if written by one person. Beyond formatting, linters like ESLint (for JS/TS, with extensions to other programming languages) identify structural errors such as unused imports and unreachable code. Static analysis tools like SonarQube go a step further by providing cognitive complexity scoring and module-level duplication detection.

As more developers use AI to write code, the importance of these basic tools grows.

GitClear’s analysis of 211 million lines of code found that the frequency of duplicated code blocks increased eightfold between 2020 and 2024, while refactoring activity dropped from 25% to under 10% of changed lines. Automated duplication detection and dead-code removal (Knip for JS/TS) in pre-commit hooks and CI/CD pipelines are no longer optional.

Without these gates, AI-generated code that compiles and passes tests can still introduce structural rot (for example, duplicated logic and dead code that accumulates silently). Engineers have started calling this “AI slop,” code that works today but degrades the codebase tomorrow.

However, automation cannot enforce architectural intentions or name conventions that communicate business intentions. Human judgment is required for both.

Version Control Hygiene

Clean commits make code reviews faster. When each commit covers a single logical change with a clear description, reviewers spend less time figuring out what happened and more time evaluating whether it should have happened. Tools like commitlint validate the commit message format in pre-commit hooks. But the substance behind the format, i.e., writing a description that actually explains the reasoning, still depends on habits.

Git history is an underrated artifact for maintainability. An atomic commit covers a single logical change, nothing more. A semantic commit message (following the conventional commits standard) is one that the next engineer can grep for by type and scope. The commit description explains the “why” behind the change.

Well-written PR descriptions that detail design decisions made provide excellent fodder for automatic changelog generation. Use stacked PRs (GitHub Stacked PRs) for large feature implementations so each PR is reviewable within the 200-400 LoC range.

Teams that treat git history as a dump forget that other programmers who inherit the code base will read it as context for changes.

The Primary Enforcement Mechanism: Code Reviews

The SmartBear/Cisco study (2,500 reviews, 3.2 million lines of code, 50 developers) identified firm cognitive bounds for reviewers to effectively inspect and find defects.

Reviewers should limit themselves to inspecting 200-400 lines of code (LoC) per review, at no more than 500 LoC/hour, for a maximum of 60-90 minutes. Once these bounds are exceeded, defect detection drops off dramatically. Consider setting PR size limits programmatically as a form of cognitive hygiene.

The study also demonstrated that “author preparation,” in which authors include comments in the PR describing which files to examine first and why the code was written the way it was, led to the discovery of more defects. When PR authors describe their own changes, they force themselves to review their work.

Beyond identifying defects, code reviews build a shared understanding of the codebase among all team members. When teams regularly review each other’s work, checklists created by individual team members based on their frequent mistakes can transform reviews into training opportunities: when a senior reviewer examines a junior team member’s PR against that checklist, the senior reviewer is providing coaching, and over time, that checklist will shrink as team members learn those patterns.

Pair programming provides similar benefits to building a common understanding and eliminates delays in the asynchronous PR cycle associated with developing complex logic. Both are mentoring mechanisms as much as quality gates.

Enforcement targets structural and stylistic problems at the point of authoring. Testing targets functional correctness: does the code actually do what it’s supposed to?

Test Strategy: What to Fund and Where

Each test type has its own benefits at different points in the design.

• Business logic that is clean, with defined input and output parameters, benefits most from unit testing.
• Integration tests are useful for testing points of contact with other systems, such as databases or calls to other services.
• Distributed systems use contract tests to ensure that all interactions between services meet established expectations.
• E2E tests have limited utility due to their slowness and brittleness, and should be used only for mission-critical user journeys.
• Test-Driven Development (TDD: Red, Green, Refactor) is effective for developing well-defined business rules where the interface is stable. When interfaces are still evolving, integration tests catch the same regressions from the outside and at lower cost.
• Mutation testing is the one method of testing your tests. Tools such as Stryker can make small code changes (e.g., changing >= to < or modifying a return statement). This creates a mutated version of the original code. If your test suite does not fail when run against the mutated code, then there is an issue your test coverage metrics did not identify.

At scale, flaky tests destroy confidence in the entire test suite. These types of tests should be quarantined, and you should track the percentage of flaky tests as a team metric.

No amount of writing tests will ever guarantee the absence of bugs. Testing can only demonstrate that bugs exist. Tests act as a safety net: even good code can break when shipping new features, but even a 95% covered application can leave critical edge cases untested in production.

At some point, every team has to decide how much effort goes into refactoring existing code versus shipping new functionality. That’s a budget decision.

Refactoring as a Budgeted Ongoing Expense

Refactoring is an iterative process that benefits from established design patterns. Budget for it the same way you budget for IT infrastructure maintenance.

A concrete example: an 80-hour refactor that saves each developer 5 hours of monthly friction on a US-based 10-person team recovers 50 developer-hours per month. At an average salary of $150K (roughly $75/hour), the 80 invested hours pay back in under 2 months and free up about $45,000 in annual productivity. This kind of break-even analysis is how you justify refactoring budgets to leadership.

Two patterns work in practice.

• First, allocate 15-20% of each sprint to debt reduction, protected from feature pressure.
• Second, schedule dedicated cleanup cycles.

37signals calls theirs a “bug smash”, a full cycle dedicated to fixing bugs and resolving long-standing issues, typically around the holidays when normal project momentum stalls anyway. Both avoid the catastrophic alternative: the “big rewrite” that routinely exceeds budgets and finishes years late while the business stalls.

McKinsey’s Tech Debt Score analysis of 220 companies confirms the connection between code quality and business performance. Organizations in the 80th percentile for managed technical debt achieve 20% higher revenue growth than those in the bottom 20th percentile.

Sometimes, though, the existing code you need to refactor is code you didn’t write and can’t rewrite from scratch. That’s where refactoring strategy turns into legacy strategy.

Legacy System Refactoring (When You Can’t Just Rewrite)

When a full rewrite is off the table (and it usually should be), the Strangler Fig pattern provides a phased alternative.

Coined by Martin Fowler in 2004, this design pattern describes modifying existing code incrementally, replacing the legacy system piece by piece:

• Place a proxy layer in front of the legacy system,
• Extract the highest-friction components first and rebuild them as independent services,
• Redirect traffic through the proxy as each component goes live.
• The legacy system is gradually starved rather than replaced in a single risky cut.

Each extraction is independently testable and reversible. You realize ROI incrementally on the most painful components first, rather than betting the modernization budget on a multi-year project.

The refactoring budget, whether applied to fresh code or legacy systems, assumes you know where the problems are. That requires measurement.

Measuring Whether It’s Working

Four signals give engineering leaders a concrete read on codebase health:

DORA tracks four metrics: deployment frequency, lead time (first commit to production), change failure rate (the percentage of deployments that cause an incident), and time to restore service.

But DORA alone has a blind spot: high deployment frequency with declining code health means you’re shipping debt faster. Therefore, pair DORA with structural metrics. For example, the cognitive complexity can be tracked per module using SonarQube or CodeClimate. The technical debt ratio (the estimated cost to fix all code issues divided by the cost to rewrite) gives a single number that trends over time. When structural metrics rise while DORA looks healthy, the team is borrowing against its own velocity.

These signals apply equally to internal teams and external partners. The difference is how you enforce them across organizational boundaries.

Maintaining Standards Across Vendors and Distributed Teams

Standards that exist only in documentation get ignored. For vendor and contractor teams, enforcement requires three mechanisms.

Specify everything at the contract level. For example, include complexity thresholds and minimum mutation test kill rates in the technical SLA; add PR size limits. Define what “maintainable” means in measurable terms beyond “deliver working code.”

Verify in technical screens. Use a small, representative codebase task during vendor evaluation. Assess naming consistency, test coverage choices, and commit hygiene to determine if the vendor can write clean, descriptive names and produce clear code.

Catch drift throughout the engagement, long before the final handoff. Require vendor teams to participate in your code review process throughout the engagement, rather than running a separate one. Shared review builds shared standards. Enforce the same static analysis gates in local development environments as in CI/CD. Track time-to-first-PR for vendor engineers the same way you would for new internal hires.

An engineering leader who can articulate measurable quality criteria and verify them in a CI pipeline closes a persistent outsourcing gap: code that passes acceptance tests but is impossible to maintain after the contract ends.

What It All Comes Down To

Teams that practice maintainability spend less time debugging and onboard engineers more rapidly. Organizations that skip these best practices pay in production incidents and in refactoring projects that eat the budget meant for new features.

Every engineering organization has developers who care about code quality. Whether leadership gives them the budget and the process to act on it determines whether you end up with maintainable software or spaghetti code that fights you at every turn.