Benchmark: Extract number from string - fixed

Script Preparation code:

var testString = "123.456 unit"

Tests:

Splitting
var values = testString.split(" "); var value1 = values[0];
parseFloat
var value1 = Number.parseFloat(testString);
regex
var value1 = testString.match(/[-\d.]*/);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Splitting
parseFloat
regex

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):

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Let's break down the provided JSON data and explain what's being tested, compared, and other considerations.

**Benchmark Definition**

The benchmark definition represents a JavaScript microbenchmark that measures the performance of extracting numbers from strings using three different approaches:

1. **Splitting**: This approach uses the `split()` method to split the input string into an array of substrings, and then extracts the first substring.
2. **parseFloat**: This approach uses the `Number.parseFloat()` function to parse the input string as a floating-point number.
3. **Regex**: This approach uses regular expressions (regex) to extract numbers from the input string.

**Script Preparation Code**

The script preparation code provides a sample input string, `testString = "123.456 unit"`, which is used as the input for the benchmark.

**Html Preparation Code**

There is no HTML preparation code provided, which means that the benchmark does not involve rendering web pages or any complex UI-related operations.

**Individual Test Cases**

The individual test cases represent each of the three approaches mentioned earlier:

1. **Splitting**: This test case uses the `split()` method to extract numbers from the input string.
2. **parseFloat**: This test case uses the `Number.parseFloat()` function to parse the input string as a floating-point number.
3. **Regex**: This test case uses regex to extract numbers from the input string.

**Latest Benchmark Results**

The latest benchmark results show the performance of each approach on a Chrome 114 browser running on Linux, with a device platform of Desktop and an operating system of Linux:

1. **Regex**: 8151470.0 executions per second
2. **Splitting**: 6753659.5 executions per second
3. **parseFloat**: 4341812.0 executions per second

**Comparison**

The comparison between these approaches is based on their performance in terms of the number of executions per second. The approach that performs best will be considered the winner.

**Pros and Cons of Each Approach**

1. **Splitting**:
	* Pros: Simple, efficient, and widely supported.
	* Cons: May not work correctly for all input strings (e.g., strings with non-numeric characters).
2. **parseFloat**:
	* Pros: Accurate, robust, and flexible.
	* Cons: May be slower than splitting for large input strings due to the overhead of parsing.
3. **Regex**:
	* Pros: Powerful, flexible, and accurate.
	* Cons: Can be slow for very large input strings or complex regex patterns.

**Other Considerations**

1. **Libraries**: The `Number.parseFloat()` function is a built-in JavaScript function that does not require any libraries.
2. **Special JS Features/Syntax**: None mentioned in the benchmark definition.
3. **Alternatives**: Other approaches to extract numbers from strings might include:
	* Using a library like ` numeral.js` or ` parse-float`.
	* Implementing custom regex patterns for specific use cases.

In summary, this benchmark compares three different approaches to extract numbers from strings: splitting, `parseFloat`, and regex. The comparison is based on performance in terms of executions per second, with the approach that performs best being considered the winner.

Related benchmarks:

Extract number from string - fixed (version: 0)

Comparing performance of: Splitting vs parseFloat vs regex

Created: 2 years ago by: Guest

Jump to the latest result

Splitting

parseFloat

regex

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):