Benchmark: Function vs () =>

Tests:

Function
function use(a) { return a * 10; } use(10); use(20); use(30);
() =>
const use = (a) => a * 10; use(10); use(20); use(30);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Function
() =>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain the provided benchmark.

**What is being tested?**

MeasureThat.net is testing two different approaches for creating small, reusable functions: traditional function syntax (e.g., `function use(a) { ... }`) and arrow function syntax (e.g., `(a) => a * 10;`).

**Options compared:**

The benchmark compares the execution speed of these two approaches. The idea is to see which one is faster when performing simple arithmetic operations.

**Pros and cons of each approach:**

* Traditional function syntax:
	+ Pros: More explicit, readable code. Easier to understand the intent behind a function.
	+ Cons: Can be more verbose and slower due to the need to declare a new scope with `function`.
* Arrow function syntax:
	+ Pros: Concise, expressive code. Reduces boilerplate and can improve readability.
	+ Cons: May not be as clear about the intent of a function, especially for complex operations.

**Library or framework usage:**

In this benchmark, there is no explicit library or framework mentioned. However, some older JavaScript implementations (e.g., Internet Explorer) used to have different behavior when it came to arrow functions due to their implementation details.

**Special JS feature or syntax:**

The benchmark does not explicitly test any special JavaScript features or syntax beyond the two approaches for creating small functions. If you were interested in testing other aspects, you might consider exploring things like:

* `let` vs `const` vs `var`
* Function scope and closures
* Hoisting
* Asynchronous programming (e.g., promises, async/await)

**Other alternatives:**

If you wanted to explore alternative approaches for creating small functions, you could also consider:

* **Lambdas**: Some languages, like Haskell or Scala, use lambdas as a way to create small, anonymous functions. MeasureThat.net doesn't support these examples directly.
* **Closures**: Closures are objects that "remember" their surrounding scope, which can be useful for creating small functions. You could test how closures compare to traditional function syntax and arrow functions.

**Benchmark preparation code:**

The provided script preparations codes demonstrate the differences between traditional function syntax and arrow function syntax:

* `function use(a) { ... }` creates a new function named `use`.
* `(a) => a * 10;` creates an anonymous function that takes a single argument, `a`, and returns its value multiplied by 10.

The benchmark runs these scripts multiple times to collect execution data.

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36

Browser/OS: Chrome 122 on Windows

View result in a separate tab

Test name	Executions per second
Function	198826512.0 Ops/sec
() =>	196434480.0 Ops/sec

Related benchmarks:

Function vs () => (version: 0)

Comparing performance of: Function vs () =>

Created: 8 months ago by: Guest

Jump to the latest result

Function

() =>

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):