Clean Code: 10 Rules I Actually Follow (And 5 I Ignore)

I've been staring at other people's code for 14 years now as a senior software architect at a mid-sized fintech company, and I can tell you exactly when I stopped being a clean code zealot: it was 2:47 AM on a Tuesday in March 2019, when our payment processing system went down because someone had spent three days refactoring a perfectly functional module to follow every single rule in Uncle Bob's book. The irony? The bug was introduced during the "cleanup."

That night changed how I think about code quality. I'm not saying clean code principles are wrong—far from it. But after reviewing over 10,000 pull requests, mentoring 47 developers, and shipping 23 major product releases, I've learned that dogmatic adherence to any set of rules is just another form of technical debt. Some clean code rules are absolute gold. Others? They're context-dependent at best, and actively harmful at worst.

Here's what I actually do in production code, and more importantly, why I do it.

Rule I Follow #1: Functions Should Do One Thing (But I Define "One Thing" Differently)

The single responsibility principle for functions is probably the most valuable rule I follow religiously. But here's where I diverge from the textbook: I don't measure "one thing" by lines of code or the number of operations. I measure it by conceptual cohesion.

Last quarter, I reviewed a function that was 8 lines long but violated SRP spectacularly. It validated user input AND logged the validation result AND updated a cache. Three distinct responsibilities crammed into 8 lines. Compare that to a 45-line function I wrote last month that orchestrates a complex database transaction—it does "one thing" (complete a payment transaction), but that one thing requires multiple steps that belong together.

Here's my litmus test: Can I describe what this function does in a single sentence without using the word "and"? If I need to say "this function validates the input AND sends an email," it's doing two things. But if I say "this function processes a refund request," and that naturally involves validation, database updates, and notification—that's still one thing at the right level of abstraction.

In practice, this means my functions average 25-30 lines instead of the 10-15 that purists recommend. But our bug rate on these functions is 40% lower than on the over-extracted code we had before. Why? Because keeping related operations together reduces the cognitive load of understanding the system. When everything is split into tiny functions, you spend more time jumping between files than understanding business logic.

The real win here is testability. A function that does one conceptual thing is easy to test, even if it's 40 lines long. You mock the dependencies, call the function, assert the outcome. Done. When you've extracted everything into 5-line functions, you end up with integration tests anyway because unit testing becomes meaningless.

Rule I Follow #2: Meaningful Names Are Non-Negotiable

I will die on this hill: variable and function names are the most important documentation you'll ever write. I've rejected pull requests solely because of poor naming, and I'll do it again.

"Dogmatic adherence to any set of rules is just another form of technical debt. The best code isn't the cleanest—it's the code that ships reliably and can be maintained by your team."

Two months ago, a junior developer submitted code with a function called `processData()`. I sent it back with a 10-minute Loom video explaining why. That function was specifically validating payment card numbers against the Luhn algorithm. The correct name was `validateCardNumberChecksum()`. Yes, it's longer. Yes, it's more specific. That's exactly the point.

Here's my naming hierarchy, refined over thousands of code reviews:

• Boolean variables: Always start with is/has/can/should. Not `active`, but `isActive`. Not `permission`, but `hasPermission`.
• Functions: Verbs for actions, nouns for queries. `calculateTotalPrice()` not `totalPrice()`. `getUserById()` not `user()`.
• Classes: Nouns that represent concepts, not actions. `PaymentProcessor` not `ProcessPayments`.
• Constants: SCREAMING_SNAKE_CASE for true constants, camelCase for configuration that might change.

The impact is measurable. After implementing strict naming conventions on our team 18 months ago, our average PR review time dropped from 3.2 hours to 1.8 hours. Why? Because reviewers spend less time deciphering what code does and more time evaluating whether it does it correctly.

I also enforce a "no abbreviations" rule with exactly three exceptions: `id`, `url`, and `api`. Everything else gets spelled out. `usr` becomes `user`. `btn` becomes `button`. `calc` becomes `calculate`. The extra keystrokes are worth it when someone is debugging at 11 PM and doesn't have to guess what `tmpBfr` means.

Rule I Follow #3: Comments Explain Why, Not What

I've seen two extremes in my career: codebases with zero comments and codebases where every line has a comment. Both are wrong, but the over-commented code is actually worse because it creates a maintenance burden and often lies.

My rule is simple: if you're explaining what the code does, the code is probably bad. If you're explaining why you made a specific decision, that's a good comment. Here's a real example from our codebase:

Bad comment: "Loop through all users and check if they're active." The code already says that. Delete the comment.

Good comment: "We process users in batches of 500 instead of all at once because the email service rate-limits us at 1000 requests per minute. Tested batch sizes of 100, 500, and 1000—500 gave the best throughput without hitting rate limits. See ticket PERF-2847 for benchmarks."

That second comment is gold. It explains a non-obvious decision, provides context for future maintainers, and links to additional information. Six months from now, when someone wonders why we're not processing all users at once, they'll understand immediately.

I also use comments for TODOs, but with a strict format: `TODO(username, YYYY-MM-DD): Description and ticket number`. This makes TODOs searchable and accountable. We have a monthly script that flags TODOs older than 90 days, and they either get done or deleted. No zombie TODOs allowed.

The exception to my "no what comments" rule is complex algorithms. If you're implementing a binary search tree rebalancing algorithm or a cryptographic function, comment the hell out of it. Future you will thank present you.

Rule I Follow #4: Keep Functions and Classes Small (With Nuance)

Yes, I follow this rule, but not blindly. The clean code purists will tell you functions should be 5-10 lines and classes should be under 200 lines. I think that's often too aggressive, but the principle is sound: smaller units are easier to understand and test.

"I don't measure 'one thing' by lines of code or the number of operations. I measure it by conceptual cohesion. A 45-line function can have a single responsibility if all those lines serve one clear purpose."

My actual limits, based on what works for our team:

• Functions: 50 lines is my yellow flag, 100 lines is my red flag. Most functions should be 20-40 lines.
• Classes: 500 lines is my yellow flag, 1000 lines is my red flag. Most classes should be 200-400 lines.
• Files: 800 lines is my yellow flag, 1500 lines is my red flag.

These aren't arbitrary. I analyzed our bug database last year and found that functions over 100 lines had 3.7x more bugs per line than functions under 50 lines. Classes over 1000 lines had 4.2x more bugs per line than classes under 500 lines. The correlation is real.

But here's the nuance: I'd rather have one 80-line function that's cohesive than four 20-line functions that require jumping between files to understand. The goal isn't small functions—it's understandable functions. Sometimes those align, sometimes they don't.

I also make exceptions for specific patterns. Data transfer objects (DTOs) can be large—I've got a 600-line DTO for our payment API response that's just property definitions. That's fine. It's not complex, it's just comprehensive. Similarly, configuration classes can be large if they're just declaring constants.

The key is to measure complexity, not just size. A 200-line function with 15 nested if statements is a nightmare. A 200-line function that's a switch statement with 40 simple cases is usually fine.

Rule I Follow #5: Write Tests First (Sometimes)

I'm a pragmatic TDD practitioner, which means I don't always write tests first, but I always write tests. The "when" depends on what I'm building.

For bug fixes, I write the test first 100% of the time. The test proves the bug exists, then I fix the code until the test passes. This has saved us from regression bugs countless times. We have a policy: no bug fix PR is accepted without a test that would have caught the bug.

For new features, I use a hybrid approach. If I'm implementing a well-defined algorithm or business rule, I write tests first. If I'm exploring a new API or prototyping a UI, I write tests after. The key is that tests get written before the PR is submitted.

Our test coverage target is 80% for business logic, 60% for integration code, and 40% for UI code. These aren't arbitrary—they're based on where bugs actually occur. In our system, 73% of production bugs in the last two years were in business logic, 19% in integration code, and 8% in UI code. We test accordingly.

I also enforce test quality standards. A test that just calls a function and asserts it doesn't throw an exception is worthless. Good tests verify behavior, not implementation. They test edge cases, error conditions, and boundary values. Our test review checklist includes: Does it test the happy path? Does it test at least two error conditions? Does it test boundary values?

The ROI on testing is clear in our metrics. Features with 80%+ test coverage have 5.1x fewer production bugs than features with under 50% coverage. The time spent writing tests is recovered within the first month of production deployment.

Rule I Ignore #1: DRY (Don't Repeat Yourself) Is Not Absolute

Here's where I break from clean code orthodoxy: I don't eliminate all duplication. In fact, I often intentionally duplicate code, and I can prove it's the right decision.

"The irony of clean code zealotry: spending three days refactoring a perfectly functional module only to introduce the bug that takes down your production system at 2:47 AM."

The DRY principle says every piece of knowledge should have a single representation in your system. Sounds great in theory. In practice, it leads to premature abstraction and coupling that makes code harder to change.

I follow the "rule of three": if I see the same code twice, I leave it. If I see it three times, then I consider abstracting it. But even then, I ask: is this duplication accidental or essential?

Accidental duplication is when two pieces of code happen to look similar but represent different concepts. Example: two functions that both validate email addresses. That's accidental duplication—they should share a validation function.

Essential duplication is when two pieces of code look similar but represent different concepts that might evolve independently. Example: the validation rules for a user email during registration versus during profile update. They might be identical today, but they represent different business contexts and might diverge tomorrow.

Last year, we had a "shared validation library" that was used by 12 different services. It seemed like good DRY practice. Then the payment team needed to add a new validation rule that didn't apply to other services. They had three options: add a parameter to make the validation conditional (complexity), create a new function (duplication), or just copy the code (heresy!).

They chose option three, and it was the right call. Six months later, the payment validation had diverged significantly from the other services. If they'd kept using the shared library, it would have become a mess of conditional logic and special cases.

My guideline: prefer duplication over the wrong abstraction. It's easier to abstract duplicated code later than to de-abstract a bad abstraction.

Rule I Ignore #2: No Comments Means Self-Documenting Code

The clean code movement has this idea that if your code needs comments, it's not clean enough. I think that's nonsense, and I've got the data to prove it.

I ran an experiment last year. I took 20 functions from our codebase—10 with detailed comments explaining the why, and 10 with no comments but "self-documenting" code. I asked 8 developers (mix of junior and senior) to explain what each function did and why it was implemented that way.

For the commented functions, developers got it right 87% of the time. For the uncommented functions, they got it right 34% of the time. Even the "self-documenting" code wasn't documenting itself well enough.

The problem is that code can tell you what it does, but it can't tell you why it does it that way. It can't tell you what alternatives were considered. It can't tell you about the business context or the constraints that led to this solution.

Here's a real example from our codebase. We have a function that processes refunds with a 72-hour delay. The code is clean and readable—you can see exactly what it does. But without the comment explaining that the delay is required by our payment processor's fraud detection system, you'd think it was a bug or an arbitrary decision.

I've seen developers "fix" that delay by removing it, only to have fraud detection break. The comment prevents that. No amount of clean code can replace that context.

My rule: write code that's as clear as possible, then add comments for anything that isn't obvious from the code itself. Business rules, performance considerations, security requirements, integration constraints—all of these deserve comments.

Rule I Ignore #3: Avoid Else Statements

There's a clean code principle that says you should avoid else statements and use early returns instead. The idea is that early returns reduce nesting and make code more readable. I think this is sometimes true and sometimes false.

Early returns are great for validation and error handling. If you're checking preconditions, absolutely use early returns:

Good: Check if user is null, return error. Check if user is inactive, return error. Process user.

But for business logic with multiple branches, else statements are often clearer. Consider a function that calculates shipping cost based on weight. You could use early returns, but an if-else chain or switch statement is more readable because it shows all the options at once.

I've found that developers spend less time understanding code with explicit else statements when there are multiple logical branches. The structure makes it clear that these are mutually exclusive options.

My guideline: use early returns for error conditions and guard clauses. Use else statements for business logic with multiple valid paths. The goal is readability, not adherence to a rule.

Rule I Ignore #4: Classes Should Be Small and Focused

I mentioned earlier that I keep classes reasonably small, but I don't follow the extreme version of this rule that says classes should be tiny and hyper-focused. Sometimes a larger class is the right choice.

We have a PaymentProcessor class that's 847 lines. According to clean code purists, this is a monstrosity that should be split into multiple classes. But : it works beautifully, it's well-tested, and it's easy to understand.

The class handles the entire payment processing workflow: validation, authorization, capture, refunds, and error handling. Yes, those are different responsibilities. But they're all part of the same business process, they share state, and they need to coordinate with each other.

We tried splitting it once. We created separate classes for validation, authorization, capture, and refunds. The result was a mess of classes passing data back and forth, with unclear ownership of the payment state. We spent two weeks on the refactor, then reverted it because it made the code harder to understand and test.

The lesson: cohesion matters more than size. If a class represents a single business concept and its methods work together on shared state, it's fine for it to be large. The alternative—splitting it into multiple classes that are tightly coupled anyway—is worse.

Rule I Ignore #5: Avoid Primitive Obsession

Clean code advocates say you should wrap primitives in domain objects. Instead of passing around strings for email addresses, create an EmailAddress class. Instead of using integers for money, create a Money class.

I think this is overkill in most cases, and I've got the performance data to back it up. We tried the "no primitives" approach on a high-throughput service last year. We wrapped everything: EmailAddress, PhoneNumber, UserId, TransactionId, Money, Percentage, etc.

The result? Our memory usage increased by 34%, our garbage collection time increased by 28%, and our throughput decreased by 12%. All because we were creating millions of tiny objects instead of using primitives.

We rolled back most of it. Now we use domain objects only when they add real value: when there's validation logic, when there are operations specific to that type, or when we need to prevent mixing up similar types (like different kinds of IDs).

For simple values that are just data, primitives are fine. An email address is just a string. A user ID is just a number. Wrapping them in objects doesn't make the code cleaner—it makes it more verbose and slower.

My rule: create domain objects when they encapsulate behavior or enforce invariants. Use primitives when they're just data. Don't create objects for the sake of avoiding primitives.

The Real Clean Code Principle: Context Over Dogma

After 14 years and over 2 million lines of code reviewed, here's what I've learned: clean code isn't about following rules. It's about making code that's easy to understand, easy to change, and easy to maintain. Sometimes that means following the textbook rules. Sometimes it means breaking them.

The rules I follow—meaningful names, focused functions, good tests, explanatory comments—these have proven their value in production. They reduce bugs, speed up development, and make onboarding easier. I've got the metrics to prove it.

The rules I ignore—extreme DRY, no comments, tiny classes, no primitives—these often make code worse in practice. They add complexity, reduce performance, or obscure intent. I've got the metrics to prove that too.

The key is to understand the principles behind the rules, then apply them with judgment. Ask yourself: does this make the code easier to understand? Does it make it easier to change? Does it reduce bugs? If the answer is yes, do it. If the answer is no, don't.

Clean code is a means to an end, not an end in itself. The end is software that works, that's maintainable, and that delivers value to users. Everything else is just technique.

So yes, I follow clean code principles. But I follow them pragmatically, not dogmatically. And that's made all the difference in the quality and velocity of the code my team ships.

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