Benchmark: Tempasdfasdfasdfaqewrqewr

Script Preparation code:

var list = [];
for (var i = 0; i < 1000 * 1000; i++) {
    list.push(i);
}

Tests:

splice
var entries = list.splice(0, 100)
slice
var entries = list.slice(0, 100) list = list.slice(100)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
splice
slice

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its test cases.

**Benchmark Definition JSON**

The benchmark definition JSON provides the basic information about the benchmark, including:

* `Name`: A unique name for the benchmark.
* `Description`: An optional description of the benchmark. In this case, it's empty.
* `Script Preparation Code` and `Html Preparation Code`: These are used to prepare the JavaScript environment for running the test cases.

In this case, the script preparation code creates an array `list` with 1 million elements using a `for` loop. The `Html Preparation Code` is empty, which means no HTML is prepared for the benchmark.

**Test Cases**

The test cases are represented as individual objects within an array:

* Each object contains:
	+ `Benchmark Definition`: A single JavaScript statement that defines the behavior to be tested.
	+ `Test Name`: A human-readable name for the test case.

There are two test cases:

1. **splice**: This test case is defined by the `benchmark definition` as `var entries = list.splice(0, 100)`.
2. **slice**: This test case is defined by the `benchmark definition` as `var entries = list.slice(0, 100)\r\nlist = list.slice(100)`.

**Options Compared**

In this benchmark, two different approaches are being compared:

1. **splice**: Uses the `splice()` method to remove the first 100 elements from the array.
2. **slice**: Uses the `slice()` method to create a new array containing the first 100 elements of the original array, and then reassigns the remaining elements to the same array using another call to `slice()`.

**Pros and Cons**

* **splice**:
	+ Pros: Efficient use of memory (only one allocation).
	+ Cons: Modifies the original array, which can be unexpected behavior.
* **slice**:
	+ Pros: Creates a new array object with the desired elements, leaving the original array unchanged.
	+ Cons: Requires two separate allocations.

In general, `splice` is faster for small arrays, but `slice` is more predictable and safer when working with larger datasets.

**Library**

Neither of these methods relies on any external libraries. However, if you're using an older version of JavaScript that doesn't support modern array methods like `splice()` or `slice()`, you might need to use a polyfill or alternative implementation.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax being used in these test cases.

**Other Alternatives**

If you wanted to test similar algorithms, you could consider the following alternatives:

1. **indexOf() and shift()**: Instead of using `splice()` or `slice()`, you could use `indexOf()` to find the index of the first element to remove or replace, and then use `shift()` to remove the elements.
2. **Array.prototype.indexOf()` with array concatenation: You could concatenate two arrays using the `concat()` method instead of creating a new array with `slice()`.
3. **Array.prototype.map()**: If you're only interested in processing the first 100 elements, you could use `map()` to create an array with those elements and then process it further.

Keep in mind that these alternatives might have different performance characteristics or side effects compared to using `splice()` or `slice()`.

Related benchmarks:

Tempasdfasdfasdfaqewrqewr (version: 0)

Comparing performance of: splice vs slice

Created: 5 years ago by: Guest

Jump to the latest result

splice

slice

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):