Benchmark: demoasd - MeasureThat.net

Script Preparation code:

var someFloat = 0.123456789;

Tests:

one
someFloat.toFixed(4);
two
Math.round(someFloat*10000) / 1000

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
one
two

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark JSON and explain what's being tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark definition is a JSON object that contains metadata about the test case. Here's a breakdown:

* `Name`: A unique identifier for the benchmark.
* `Description`: A brief description of the benchmark.
* `Script Preparation Code` (SPC): A JavaScript code snippet that sets up variables and environments for the actual benchmarking code to run in.
* `Html Preparation Code` (HPC): An optional HTML code snippet used to create a test page, but it's empty in this case.

**Individual Test Cases**

There are two individual test cases:

1. `"someFloat.toFixed(4);"`:
	* This is the benchmarking function being tested.
	* `someFloat` is a variable set up by the `Script Preparation Code`.
2. `"Math.round(someFloat*10000) / 1000"`:
	* This is another test case, which appears to be testing the same functionality as the first one but with some modifications.

**Library**

There isn't an explicit library mentioned in the benchmark JSON, but it's likely that `someFloat` is a global variable or a variable from a commonly used JavaScript library.

**Special JS Feature/Syntax**

The test cases use the `toFixed()` function and `Math.round()`, which are standard JavaScript functions. There's no indication of any special features or syntax being tested.

**Comparison Options**

Based on the two test cases, we can compare:

1. **Precision vs. Optimality**: The first test case uses `toFixed(4)`, while the second test case uses `Math.round(someFloat*10000) / 1000`. The former ensures a fixed precision of 4 decimal places, whereas the latter may be more optimal in terms of computation but might introduce rounding errors.
2. **Approach to Rounding**: Both methods use rounding, but they do so differently (fixed vs. arbitrary number of significant figures). The choice between these approaches depends on the specific requirements of the application.

**Pros and Cons**

Pros:

* The first test case ensures a fixed precision, which is important for applications requiring precise calculations.
* The second test case might be faster or more efficient due to its use of `Math.round()` with an arbitrary number of significant figures.

Cons:

* The second test case may introduce rounding errors, depending on the value of `someFloat`.
* The choice between these two approaches depends on the specific requirements and constraints of the application.

**Other Alternatives**

Some alternative approaches for testing rounding functions could include:

1. Using a library like `decimal.js` to perform precise decimal arithmetic.
2. Testing with different inputs or edge cases, such as very large or small numbers, to evaluate the behavior of `toFixed()` and `Math.round()`.
3. Comparing the performance of these functions against other alternatives, such as using assembly language or native code.

Keep in mind that this is a simplified analysis, and there may be more nuances depending on the specific requirements and constraints of the application being tested.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.141 YaBrowser/22.3.3.866 Yowser/2.5 Safari/537.36

Browser/OS: Yandex Browser 22 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
one	4860598.5 Ops/sec
two	4591747.0 Ops/sec

Related benchmarks:

demoasd (version: 0)

demo

Comparing performance of: one vs two

Created: 2 years ago by: Guest

Jump to the latest result

one

two

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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