Benchmark: leftpad with chatgpt and native

Script Preparation code:

function leftpad (str, len, ch) {
  str = str + "";
  len = len - str.length;
  if (len <= 0) return str;
  if (!ch && ch !== 0) ch = " ";
  ch = ch + "";
  if (ch === " " && len < 10) return cache[len] + str;
  var pad = "";
  while (true) {
    if (len & 1) pad += ch;
    len >>= 1;
    if (len) ch += ch;
    else break;
  }
  return pad + str;
}

function chatGPTLeftPad(string, len, ch) {
  string += "";
  const padding = len - string.length;
  if (padding <= 0) {
    return string;
  }
  const padChar = ch || ch === 0 ? `${ch}` : " ";
  const padString = padChar.repeat(padding);
  return padString + string;
}

function nativeLeftPad(string, len, ch) {
  return string.padStart(len, ch);
}

Tests:

leftpad
leftpad('anim',20,'0')
chatgpt
chatGPTLeftPad('anim',20,'0')
native
nativeLeftPad('anim',20,'0')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
leftpad
chatgpt
native

Fastest: N/A

Slowest: N/A

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

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Let's dive into the provided benchmark data.

**Benchmark Overview**

The provided JSON represents three test cases, each testing a different implementation of the `leftpad` function:

1. **Native Left Pad**: The native implementation uses JavaScript's built-in `padStart` method to pad a string with a specified character.
2. **ChatGPT Left Pad**: This implementation is a custom version of `leftpad` implemented by ChatGPT, which uses a combination of string manipulation and caching to achieve better performance.
3. **Custom Left Pad**: The third implementation is the original `leftpad` function provided in the benchmark definition, which implements padding logic manually.

**Options Compared**

The three implementations are compared in terms of their execution speed, measured as executions per second. This allows us to determine which implementation performs best on a specific platform (Firefox 112).

**Pros and Cons of Each Approach**

1. **Native Left Pad**:
	* Pros: Simple, efficient, and leverages built-in JavaScript functionality.
	* Cons: May not be optimized for performance-critical applications or edge cases.
2. **ChatGPT Left Pad**:
	* Pros: Optimized for performance using caching and string manipulation techniques.
	* Cons: Custom implementation requires more code and may introduce potential bugs.
3. **Custom Left Pad**:
	* Pros: Manually implemented padding logic can be optimized for specific use cases.
	* Cons: May require more development effort to achieve comparable performance.

**Library/Utility Used**

None of the implementations directly uses a third-party library. However, the `native` implementation relies on JavaScript's built-in `padStart` method, which is part of the ECMAScript standard.

**Special JS Feature/Syntax**

The custom `leftpad` function uses a few advanced JavaScript features:

* Caching: The implementation uses a cache to store padding strings, which can improve performance by reducing the number of string concatenations.
* String manipulation: The `chatGPTLeftPad` function uses string concatenation and repetition using the `repeat()` method, which is supported in most modern browsers.

**Other Alternatives**

If you're looking for alternative approaches or libraries to implement left padding, consider:

* Using a dedicated library like `string-pad` ( Node.js) or `pad-string` ( browser-compatible).
* Exploring other string manipulation techniques, such as using `Array.prototype.fill()` and `Array.prototype.reduce()`.
* Investigating custom implementations that utilize WebAssembly or other low-level optimizations.

Keep in mind that the choice of implementation depends on your specific requirements, performance needs, and desired level of complexity.

Related benchmarks:

leftpad with chatgpt and native (version: 0)

blah

Comparing performance of: leftpad vs chatgpt vs native

Created: 3 years ago by: Guest

Jump to the latest result

leftpad

chatgpt

native

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):