https://orcid.org/0000-0002-9097-2246
If you’ve been programming for a while, you’ve probably heard the term “code smell.”
It sounds vague and it is, by design.
A code smell isn’t a bug. The code works.
But something about it feels off: hard to read, risky to change, or painful to maintain.
So the natural question is:
Can code smells be measured, or are they just subjective opinions?
The short answer: yes, partially.
What a Code Smell Really Is
A code smell is a warning sign, not a diagnosis.
Just like a medical symptom:
It doesn’t guarantee a problem
But it strongly suggests one might exist
Examples:
Very long functions
Too much duplicated code
Classes that do “everything”
Deeply nested logic
Functions with too many parameters
Measuring Code Smells (Indirectly)
Code smells can’t be measured directly, but we approximate them using metrics.
1. Size & Complexity Metrics
These are the most common indicators.
Lines of Code (LOC)
Large methods/classes → Long Method, Large Class
Cyclomatic Complexity
Counts decision paths (
if,loops)High values → complex, fragile logic
Nesting Depth
Deep nesting → harder to reason about
Number of Parameters
Too many → unclear responsibilities
These don’t prove bad design—but they raise red flags.
2. Duplication Metrics
Percentage of duplicated code
Code clone detection
High duplication often signals:
Poor abstraction
Higher maintenance cost
3. Object-Oriented Design Metrics
Used mainly in Java, C#, etc.
Coupling (CBO) – how dependent classes are on each other
Cohesion (LCOM) – how focused a class’s responsibilities are
High coupling + low cohesion often points to God Classes.
Rule-Based Smell Detection
Static analysis tools use heuristics, such as:
“If a method is longer than X lines AND complexity is above Y → flag it”
Popular tools:
SonarQube
ESLint
Pylint
PMD
Checkstyle
Important:
These tools warn—they don’t judge.
Composite Scores
Some tools calculate an overall number, like the Maintainability Index (MI).
It combines:
Code size
Complexity
Low-level metrics
Useful for:
Tracking trends over time
Not useful for:
Declaring code “good” or “bad”
What Cannot Be Measured Well
Some of the most important smells resist numbers:
Poor naming
Confusing abstractions
Over-engineering
Misplaced responsibilities
These require human judgment and code reviews.
How Teams Use This in Practice
Good teams don’t chase perfect scores.
They:
Track metrics over time
Set reasonable thresholds
Use tools as early warning systems
Rely on developers to make final decisions
The Big Takeaway
Code smells are measurable signals, not absolute truths.
Metrics help you notice problems.
Experience helps you decide whether they matter.
If this topic interests you, explore:
Refactoring patterns
Static analysis tools
Software design principles
Clean Code vs. pragmatic tradeoffs
That’s where real learning begins.
