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Yes—developers can mistake tidy structure for software that is genuinely good. A codebase may use consistent formatting, small functions, and carefully named abstractions yet still solve the wrong problem, hide its main flow, or make ordinary changes unnecessarily risky. “Clean” is useful when it serves understanding and change; it is not a synonym for “good.”
Clean, clear and good describe different things
Jaideep Parashar’s DEV Community essay offers a useful distinction:
- Clean code is well structured.
- Clear code is easy to understand.
- Good code solves the right problem with an appropriate amount of complexity.
These are the essay’s working definitions, not formal industry standards. They explain why a technically polished refactor can still leave maintainers confused or slow.
| Question | What to look for | Typical failure |
|---|---|---|
| Is it clean? | Coherent structure, naming, formatting and boundaries | Rules are followed even when they add unnecessary layers |
| Is it clear? | A maintainer can follow the important path without guesswork | The real work is scattered across wrappers and indirection |
| Is it good? | The design fits the requirement, constraints and expected change | Elegance is optimized instead of the actual user or business problem |
How tidy code becomes difficult to follow
The “simple” user action spread across files
Parashar illustrates a routine user action whose implementation requires jumping through many files. Each file may look orderly: a controller delegates to a service, the service calls a use-case object, that object invokes a repository, and several interfaces or adapters sit between them. The local pieces appear clean, but the reader must reconstruct the behavior by following a chain of indirection.
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The issue is not that multiple files are always wrong. Boundaries can isolate infrastructure and make large systems safer to change. The test is whether the boundary helps a maintainer understand the main flow. If tracing one ordinary action requires constant navigation, the structure has become an obstacle rather than a map.
Abstraction can hide complexity—or relocate it
An abstraction is valuable when it hides a detail that most callers should not need to know, such as a storage driver or an external protocol. It becomes harmful when every small operation has another wrapper, interface, factory, and configuration path. The complexity has not disappeared; it has moved into the work of discovering where the behavior actually lives.
Before introducing a layer, ask what knowledge it removes from callers and what new knowledge it requires from readers. A useful abstraction makes the common path shorter to understand. A decorative abstraction merely increases the number of places a reader must inspect.
Similar code does not always belong together
Removing duplication is often presented as an automatic improvement. But two blocks can look alike while changing for different reasons. One may implement a customer-facing policy; another may satisfy an accounting or regulatory rule. Combining them creates shared coupling: a change requested by one stakeholder now risks changing behavior owned by another.
Check the reason to change
When deciding whether to extract a shared function, compare:
- Who requests changes to each behavior?
- Would the same requirement alter both implementations?
- Do they have the same error handling, data assumptions and release cadence?
- Would a future exception for one case make the shared abstraction harder to explain?
If the answers differ, a little duplication may be cheaper and safer than a coupled abstraction. Duplication is a visible cost; accidental coupling is often discovered only during a risky change.
Comments should preserve decisions, not narrate syntax
Comments add value when they record information that code cannot express: a constraint, a trade-off, or the reason a surprising value was chosen. They add little when they translate an obvious operation into English.
Parashar’s illustrative example is: We intentionally use a 5-minute window here. The payment provider can send duplicate webhook events during retry periods.
That comment explains both the decision and its rationale. It is presented as an example, not as a report of a named provider incident.
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A practical review test for “good enough” design
The essay proposes judging code by how well people can work with it. These are practical heuristics, not validated universal tests.
- Trace the main flow. Can a maintainer follow a normal request from entry point to outcome without opening a maze of unrelated files?
- Explain the important decisions. Can the reviewer say why a boundary, timeout, validation rule or unusual branch exists?
- Change one requirement safely. Is it clear which behavior should change, and can the change be tested without surprising another feature?
- Build a mental model. Could a developer who did not write the code explain its responsibilities after a reasonable reading?
These checks deliberately include comprehension and change safety, not just formatting or rule compliance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use a decision framework before refactoring
When an abstraction is likely to help
- The same concept has a stable meaning in several places.
- Callers genuinely should not know a volatile implementation detail.
- The boundary makes tests, failure handling or replacement clearer.
- The main use case becomes easier to read after extraction.
When keeping code local is wiser
- The behavior is short and already obvious where it is used.
- The similar code has different owners or reasons to change.
- The abstraction would need many flags, callbacks or special cases.
- Readers would have to navigate more files to understand a basic path.
Compare the economics
| Option | Immediate effect | Long-term question |
|---|---|---|
| Refactor now | Time spent rearranging code and updating tests | Will future feature work or bug fixing become faster and safer? |
| Leave it local | Less disruption today | Will repetition or unclear boundaries create recurring change cost? |
| Add another abstraction | More formal separation | Does the separation remove real knowledge from callers, or just add indirection? |
Martin Fowler’s often-cited economic framing is that refactoring is not about making a codebase look “sparkly”; it is about becoming faster at adding features and fixing bugs. The quotation is associated with Refactoring: Improving the Design of Existing Code. Consult an authorized edition for the definitive wording.
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Why there is no universal clean-code rule
Practitioners disagree about how much factoring and abstraction a project needs. A Hacker News discussion titled “Clean Code vs. A Philosophy Of Software Design” includes both readers who find Clean Code guidance useful and readers who warn that its rules can be over-applied. It is anecdotal discussion, not representative research or expert consensus.
The sensible conclusion is not “never abstract” or “always keep things simple.” A small script, a long-lived service and a safety-critical component have different constraints. Team familiarity, deployment risk, domain complexity and expected change all affect the right design.
A review checklist for teams
- Can a new maintainer locate the real work quickly?
- Does each abstraction hide a detail that callers should not own?
- Do shared functions represent one concept, or merely similar text?
- Are comments explaining constraints and decisions rather than syntax?
- Does the proposed refactor reduce the cost of a likely future change?
- What complexity is being removed, and what complexity is being introduced?
Using this checklist keeps style in its proper place. Formatting, naming and structure matter because they support comprehension and safe change—not because they are the outcome by themselves.
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