Benchmark: splicevspop - MeasureThat.net

Tests:

splice
const arr = [0,1,2,3,4,5] const newArr = arr.splice(-3, 3)
pop
const arr = [0,1,2,3,4,5] const newArr = new Array(3) for (i=0; i < 3; i++) { newArr[i] = arr.pop() }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
splice
pop

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

I'll break down the provided JSON and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Definition**

The benchmark definition is represented by the following JSON object:
```
{
  "Name": "splicevspop",
  "Description": null,
  "Script Preparation Code": null,
  "Html Preparation Code": null
}
```
This defines a new benchmark with no description, script preparation code, or HTML preparation code. The name of the benchmark is "splicevspop".

**Individual Test Cases**

The test cases are defined in the following JSON array:
```
[
  {
    "Benchmark Definition": "const arr = [0,1,2,3,4,5]\r\nconst newArr = arr.splice(-3, 3)",
    "Test Name": "splice"
  },
  {
    "Benchmark Definition": "const arr = [0,1,2,3,4,5]\r\nconst newArr = new Array(3)\r\nfor (i=0; i < 3; i++) {\r\n  newArr[i] = arr.pop()\r\n}",
    "Test Name": "pop"
  }
]
```
These two test cases measure the performance of two different approaches to create a new array by removing elements from an existing array.

**Approach 1: Using `splice()`**

The first test case uses the `splice()` method to create a new array. The benchmark definition is:
```javascript
const arr = [0,1,2,3,4,5]
const newArr = arr.splice(-3, 3)
```
This approach modifies the original array by removing elements from both ends and returns an array of removed elements.

**Approach 2: Using `pop()`**

The second test case uses the `pop()` method to create a new array. The benchmark definition is:
```javascript
const arr = [0,1,2,3,4,5]
const newArr = new Array(3)
for (i=0; i < 3; i++) {
  newArr[i] = arr.pop()
}
```
This approach creates a new array and then populates it by removing elements from the original array using `pop()`.

**Pros and Cons**

* **Using `splice()`:**
	+ Pros:
		- Modifies the original array in-place, which can be more efficient.
		- Returns an array of removed elements, which can be useful for further processing.
	+ Cons:
		- Can modify the original array unexpectedly if not used carefully.
		- May have a higher overhead due to the method call and parameter passing.
* **Using `pop()`:**
	+ Pros:
		- Does not modify the original array, which can be safer and more predictable.
		- May have a lower overhead due to the simple method call and return value.
	+ Cons:
		- Creates an additional object (the new array) that needs to be managed.
		- Requires multiple iterations over the original array.

**Other Considerations**

* Both approaches can have performance implications depending on the size of the input array and the number of elements being removed.
* The use of `splice()` or `pop()` can also impact memory usage, as it involves creating new arrays or modifying existing ones.
* The test cases do not account for edge cases such as empty arrays or arrays with a single element.

**Library Usage**

There is no library explicitly used in these benchmark definitions. However, the use of modern JavaScript features and methods like `splice()` and `pop()` relies on the standard library provided by the browser or environment.

**Special JS Features/Syntax**

There are no special JS features or syntax mentioned in the benchmark definitions. The tests only focus on the basic operations of creating a new array using `splice()` or `pop()`.

Related benchmarks:

splicevspop (version: 0)

Comparing performance of: splice vs pop

Created: 3 years ago by: Guest

Jump to the latest result

splice

pop

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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