Benchmark: Add item to array3

Tests:

Slice with spread
const a = ["a", "b", "d", "e"]; const newItem = "c"; const index = 2; const b = [...a.slice(0, index), newItem, ...a.slice(index)];
Slice using concat
const a = ["a", "b", "d", "e"]; const newItem = "c"; const index = 2; const b = a.slice(0, index).concat(newItem, a.slice(index));
Splice
const a = ["a", "b", "d", "e"]; const newItem = "c"; const index = 2; const b = [...a]; b.splice(index, 0, newItem);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Slice with spread
Slice using concat
Splice

Fastest: N/A

Slowest: N/A

Latest run results:

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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 test cases and explain what is being tested.

**Benchmark Context**

The benchmarks are designed to measure the performance of different approaches for inserting an item into an array in JavaScript. The tests are comparing three methods:

1. `Array.prototype.slice()`
2. Using the spread operator (`...`)
3. Using the `concat()` method

**Method 1: `Array.prototype.slice()`**

The first test case, "Slice with spread", creates a new array by taking elements from the original array using `slice(0, index)`, spreading the new item into the resulting array, and then appending the remaining elements of the original array using `...`. This approach is similar to using `Array.prototype.slice()` followed by `concat()`.

**Method 2: Using `concat()`**

The second test case, "Slice using concat", creates a new array by taking elements from the original array using `slice(0, index)`, and then concatenates the new item into the resulting array using `concat(newItem, ...)`.

**Method 3: `Array.prototype.splice()`**

The third test case, "Splice", inserts the new item at the specified index using the `splice()` method.

**Library Used**

There is no explicit library mentioned in the benchmark definition or individual test cases. However, it's likely that the tests are running on a modern JavaScript engine that supports these array methods.

**Special JS Feature/Syntax**

None of the test cases use any special JavaScript features or syntax beyond standard ECMAScript 2022 features.

**Pros and Cons of Each Approach**

Here's a brief summary:

* **`Array.prototype.slice()`**: Pros: Fast, simple. Cons: Can be slower than other methods for larger arrays due to the overhead of creating new arrays.
* **Using `concat()`**: Pros: Easy to read, flexible. Cons: Can be slower and less efficient than other methods, especially for large arrays.
* **`Array.prototype.splice()`**: Pros: Fastest, most efficient. Cons: Can modify the original array, which may not be desirable in some cases.

**Other Alternatives**

Some alternative approaches that could have been tested include:

* Using `Array.prototype.push()` and `Array.prototype.unshift()`
* Using `Array.prototype.map()` and `Array.prototype.forEach()`
* Using a custom implementation using loops or recursion

However, these alternatives are less likely to be competitive with the standard methods used in modern JavaScript engines.

**Benchmark Result Interpretation**

The benchmark results show that:

* The "Splice" test case is the fastest, followed by the "Slice with spread" and then the "Slice using concat" tests.
* The differences between the test cases are relatively small, indicating that the performance differences are likely due to minor implementation details or compiler optimizations.

Overall, these benchmarks provide a useful comparison of different array manipulation techniques in JavaScript, helping developers choose the most efficient approach for their specific use case.

Related benchmarks:

Add item to array3 (version: 2)

Comparing performance of: Slice with spread vs Slice using concat vs Splice

Created: 5 years ago by: Registered User

Jump to the latest result

Slice with spread

Slice using concat

Splice

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):