TL;DR: Don't optimize loops without a clear need and concrete real-world evidence

Problems

• Premature Optimization
• Reduced readability
• Increased complexity
• Difficult to maintain
• Slower debugging

Solutions

1. Keep it simple
2. Prioritize clarity
3. Avoid premature tweaks
4. Refactor when needed

Context

You might think optimizing every loop will improve performance, but this approach backfires when you sacrifice clarity for unproven gains.

Writing complex code to avoid hypothetical slowdowns often makes it hard for others (and your future self) to understand or debug your code.

It would be best if you prioritized readability.

Keep loops simple and only optimize when you know a bottleneck exists in real usage scenarios.

Sample Code

Wrong

# Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()]

Right

# Clearer and easier to understand
result = []
for item in items:
    if item.is_valid():
        result.append(item.process())

Detection

[X] Semi-Automatic

Look for list comprehensions or complex loop structures that optimize performance without real performance benchmark evidence.

Exceptions

• Concrete evidence on mission-critical algorithms

Tags

• Premature Optimization

Level

[X] Intermediate

AI Generation

AI tools often prioritize functional correctness so that they might produce clean, simple loops.

if you prompt AI for performance at all costs, it could create over-optimized code even for straightforward tasks.

AI Detection

With proper instructions to stress readability and maintainability, AI can detect and fix this smell by simplifying loops and choosing clarity over premature optimization.

Try Them!

Remember: AI Assistants make lots of mistakes

Conclusion

Don’t sacrifice readability by optimizing too early.

You can optimize later if a loop becomes a proven bottleneck.

Until then, clear and simple code will save time, reduce bugs, and make it more maintainable.

Relations

Disclaimer

Code Smells are my opinion.

Credits

Photo by Tine Ivanič on Unsplash

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More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason.

W. A. Wulf

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This article is part of the CodeSmell Series.