We love to improve time and space complexity by guessing not real scenarios
TL;DR: Don't optimize anything until you have a real-use scenario benchmark.
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Cover your scenarios with tests.
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Write readable (and possibly non-performant) code.
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Do a real benchmark with real user data. (No, iterating your code 100,000 times might not be a real use case).
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If you have conclusive data, you need to improve the benchmark's found bottlenecks using the Pareto principle.
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Attack the worst 20% of problems causing 80% bad performance.
In university and online courses, we learn algorithms, data structures, and computational complexity before good design rules.
We tend to overestimate the (possible) performance problems and underestimate code readability and software lifetime.
Premature optimization often has no evidence of solving real problems.
We need to surgically improve our code when the facts tell us we have a real issue.
for (k = 0; k < 3 * 3; ++k) {
i = Math.floor(k / 3);
j = k % 3;
console.log(i + ' ' + j);
}
// This cryptic piece of code iterates a
// two-dimensional array
// You don't have proof this will be useful
// In real contextsfor (outerIterator = 0; outerIterator< 3; outerIterator++) {
for (innerIterator = 0; innerIterator< 3; innerIterator++) {
console.log(outerIterator + ' ' + innerIterator);
}
}
// This is a readable double for-loop
// 3 is a small number
// No performance issues (by now)
// You will wait for real evidence[X] Manual
This is a semantic smell.
We might find the code harder to read.
- Premature Optimization
We need to stop optimizing for machines and start optimizing for human readers and code maintainers.
We need to avoid programming languages designed for premature optimization and favor robust ones.
Code Smell 06 - Too Clever Programmer
Code Smell 20 - Premature Optimization
Photo by Priscilla Du Preez on Unsplash
Optimism is an occupational hazard of programming: feedback is the treatment.
Kent Beck
Software Engineering Great Quotes
This article is part of the CodeSmell Series.