The Tool Desk
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What does “clean code” mean, and why isn’t it automatically good code?
In his DEV Community essay, Jaideep Parashar distinguishes between code that is well-structured, code that is easy to understand, and code that solves the right problem with an appropriate amount of complexity. Those are his working definitions, not a formal industry standard. They point to a useful distinction: organization is a property of code, while goodness also depends on what the code does and how well people can work with it.
A tidy file, consistent naming, and small functions can make a codebase easier to navigate. But a feature can still be difficult to understand if following one user action means jumping among many files and layers. Conversely, a compact implementation is not necessarily good if it obscures important decisions or makes safe changes difficult.
The question is therefore not whether clean code is good. It is whether a particular choice—an abstraction, a naming convention, a refactor—helps people understand and evolve the system, or merely makes its surface look orderly.
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When does structure help, and when does it get in the way?
Abstraction earns its keep when it hides incidental detail and gives a reader a useful model of the work. A well-named function can let someone follow the main flow without first understanding every low-level operation.
Too many layers can have the opposite effect. If a simple action passes through wrappers, interfaces, factories, and helper functions before reaching the code that performs the work, maintainers may struggle to locate the important behavior. Structure has helped only if it makes the system easier to reason about—not simply because each piece has a separate file or a conventional design pattern.
Parashar’s essay illustrates this tension with a user action that requires tracing across files. That example is a useful prompt for a review, not a measurement of how often such designs occur or proof that a particular file count is too high. The relevant test is whether a maintainer can follow the real path through the system without unnecessary detours.
Should similar code be combined into one abstraction?
Not automatically. Two blocks may look alike today but exist for different reasons. If they change in response to different requirements, merging them can create coupling: a change for one use case may unexpectedly affect the other, or the shared abstraction may accumulate options to handle divergent needs.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchOn the other hand, duplication can make a common rule harder to update consistently. The practical comparison is not “duplicate code versus no duplicate code” in isolation. Ask whether the pieces share the same reason to change, whether the abstraction makes their common behavior clearer, and whether a future requirement can evolve safely without complicating unrelated uses.
- Favor a shared abstraction when the behavior and the reason it changes are genuinely shared, and the abstraction makes the main flow easier to see.
- Keep the code separate when similarity is superficial or the paths are likely to evolve independently, and combining them would require confusing branches or configuration.
Neither “always abstract” nor “always keep it simple” is a reliable rule. The right choice depends on the system, the likely changes, and the understanding of the people who maintain it.
What should comments explain?
A comment is most valuable when it preserves context that the code cannot readily show: a constraint, a trade-off, or the reason a decision was made. Parashar gives this illustrative example: “We intentionally use a 5-minute window here. The payment provider can send duplicate webhook events during retry periods.” It explains the rationale for a seemingly arbitrary value; it is not a report of a named provider incident.
By contrast, a comment that merely translates an obvious operation into prose adds little. If a comment and the code disagree, readers also have to decide which one to trust. Prefer clear names and straightforward code for the mechanics; use comments to retain the reason behind non-obvious choices.
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How can a team judge whether a refactor improves the code?
Parashar proposes practical questions rather than a validated universal test. A team can use them to make a refactor discussion concrete:
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- Can someone follow the main flow without searching through needless indirection?
- Can maintainers explain the important decisions, including constraints that are not obvious from the implementation?
- Can they make a likely change safely, with a clear sense of what else it might affect?
- Can a developer who did not write the code build a mental model of it?
These questions do not prove that a design is objectively good. They help reviewers connect a proposed cleanup to the work it is meant to improve. For example, splitting a large function may help if it gives distinct behaviors meaningful names; it may hurt if it scatters one coherent operation across helpers that are harder to trace.
When is refactoring worth doing?
Refactoring has a cost now, so its case is strongest when the change makes future feature work or bug fixing faster or safer. A passage attributed to Martin Fowler in Refactoring: Improving the Design of Existing Code frames the rationale as economic: refactoring is done to become faster at adding features and fixing bugs, not to make a codebase look “sparkly.” Because that wording is available here through a third-party-hosted passage, it is better treated as a summary of the stated rationale than as a verified quotation from an authorized edition.
That framing does not mean every refactor must produce an immediate feature. It means the team should be able to identify the maintenance friction it expects to reduce: a fragile change path, repeated fixes, unclear behavior, or a feature that is unusually hard to add. If the only stated benefit is that the code will look cleaner, the case for taking on the work is incomplete.
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Is there one accepted definition of good code?
No single definition is established by this essay or by the practitioner discussion linked below. A Hacker News thread titled “Clean Code vs. A Philosophy Of Software Design” includes contrasting views: some participants emphasize project and team context, while others regard Clean Code as useful guidance when applied with judgment. A forum thread is anecdotal evidence of disagreement, not a representative survey or expert consensus.
That disagreement reinforces the useful limit of any style rule: it is guidance, not a substitute for examining a system’s purpose and the costs of a proposed design. Cleanliness matters when it serves comprehension and change. It is not, by itself, proof that the result is good.
Quick Recap
Sources and further reading
- Jaideep Parashar, “I Think We Confuse Clean Code With Good Code,” DEV Community — the essay and examples discussed above.
- “Clean Code vs. A Philosophy Of Software Design,” Hacker News — a community discussion illustrating differing practitioner views.
- Passage attributed to Martin Fowler’s Refactoring: Improving the Design of Existing Code — third-party hosted; consult an authorized edition for exact wording.
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