Benchmark: trailingZeroes removal

Script Preparation code:

function labor(value) {
  let leading0Count = 0n;
  let digitCount = 0n;
  let i = 0;
  for (; i < value.length; i++) {
    if (value[i] !== '0') {
      break;
    }
    leading0Count++;
  }
  for (; i < value.length; i++) {
    if (value[i] === '0') {
      break;
    }
    digitCount++;
  }
  if (digitCount === 0n) {
    return 0n;
  }
  return leading0Count + digitCount;
}

const trailingZeroes = /0*$/;
const empty = '';
function optim(value) {
  return BigInt(value.replace(trailingZeroes, empty).length);
}

Tests:

without
labor('000000000000000000000123456789123456789000000000000000000');
with
optim('000000000000000000000123456789123456789000000000000000000');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
without
with

Fastest: N/A

Slowest: N/A

Latest run results:

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

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

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark that tests the performance of two different approaches to remove trailing zeros from a string.

The `Script Preparation Code` section contains two functions: `labor` and `optim`. Both functions take an input `value` and return the number of leading zeros and trailing zeros in the input string, respectively. The function `labor` uses a simple loop to count the leading and trailing zeros, while the function `optim` uses a regular expression (`trailingZeroes`) to remove all trailing zeros from the input string and then returns the length of the resulting string.

The `Html Preparation Code` section is empty, indicating that no HTML preparation code is required for this benchmark.

**Test Cases**

There are two test cases:

1. **"without"`**: This test case uses the original function `labor` without any optimizations.
2. **"with"`**: This test case uses the optimized function `optim`, which removes trailing zeros using a regular expression.

**Options Compared**

The benchmark compares the performance of the two approaches:

* Approach 1: Using the `labor` function with no optimizations (`without`)
* Approach 2: Using the `optim` function, which removes trailing zeros using a regular expression (`with`)

**Pros and Cons**

**Approach 1 (without)**

Pros:

* Simple and easy to understand
* No additional dependencies required

Cons:

* May be slower due to the use of loops instead of regular expressions

**Approach 2 (with)**

Pros:

* Optimized for performance using regular expressions
* Reduced number of iterations over the input string

Cons:

* Requires an additional dependency on the `trailingZeroes` regular expression
* May be more complex to understand and maintain

**Library**

The `trailingZeroes` regular expression is used in Approach 2. This regular expression matches one or more (`0*`) zeros at the end of a string (`$`). The purpose of this library is to simplify the removal of trailing zeros from strings.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in this benchmark. The functions `labor` and `optim` use standard JavaScript constructs, such as loops, conditional statements, and regular expressions.

**Alternatives**

Other alternatives for removing trailing zeros from strings could include:

* Using a simple loop to iterate over the input string
* Using a library like Lodash or UglifyJS to optimize the removal of trailing zeros
* Using a different regular expression pattern to remove trailing zeros

However, Approach 2 (with) using the `trailingZeroes` regular expression is likely to be the most efficient and optimized solution for this specific benchmark.

Related benchmarks:

trailingZeroes removal (version: 1)

Comparing performance of: without vs with

Created: 3 years ago by: Registered User

Jump to the latest result

without

with

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):