Benchmark: testttttinnnnggg

Tests:

collectAutomatedPricingExcludedBanks
const banks = [{ excluded: false, name: 'A' }, { excluded: false, name: 'B' }, { excluded: false, name: 'C' }, { excluded: false, name: 'D' }, { excluded: false, name: 'E' }, { excluded: false, name: 'F' }, { excluded: true, name: 'G' }, { excluded: true, name: 'H' }, { excluded: true, name: 'I' }, { excluded: true, name: 'J' }, { excluded: true, name: 'K' }, { excluded: true, name: 'L' }] const collectAutomatedPricingExcludedBanks = (banks) => { const excludedBanks = [] banks.forEach((bank) => { if (bank.excluded) { excludedBanks.push(bank.name) } }) return excludedBanks } collectAutomatedPricingExcludedBanks(banks)
collectAutomatedPricingExcludedBanks2
const banks = [{ excluded: false, name: 'A' }, { excluded: false, name: 'B' }, { excluded: false, name: 'C' }, { excluded: false, name: 'D' }, { excluded: false, name: 'E' }, { excluded: false, name: 'F' }, { excluded: true, name: 'G' }, { excluded: true, name: 'H' }, { excluded: true, name: 'I' }, { excluded: true, name: 'J' }, { excluded: true, name: 'K' }, { excluded: true, name: 'L' }] const collectAutomatedPricingExcludedBanks = (banks) => { return banks.filter(bank => bank.excluded).map(bank => bank.name) } collectAutomatedPricingExcludedBanks(banks)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
collectAutomatedPricingExcludedBanks
collectAutomatedPricingExcludedBanks2

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and analyze what's being tested.

**Benchmark Definition**

The benchmark definition is an empty object with no specific details about the script or tests being run. This is likely due to the fact that the test cases are already defined in the individual test case objects.

**Individual Test Cases**

There are two test cases:

1. `collectAutomatedPricingExcludedBanks`:
	* The benchmark definition uses a JavaScript array `banks` with 12 elements, some of which have an `excluded` property set to `true`.
	* The test function `collectAutomatedPricingExcludedBanks` takes the `banks` array as input and returns an array of names from the excluded banks.
	* There are two possible implementations for this test case:
		+ The first implementation uses a `forEach` loop to iterate over the `banks` array and push the name of each excluded bank into the `excludedBanks` array.
		+ The second implementation uses the `filter()` and `map()` methods to create a new array containing only the names of the excluded banks.
2. `collectAutomatedPricingExcludedBanks2`:
	* This test case is identical to the first one, with the same benchmark definition and test function.

**What's being tested?**

Both test cases are testing the performance of the `collectAutomatedPricingExcludedBanks` function under different implementations. The test is likely checking which implementation is faster for large input datasets.

**Options compared**

The two implementations being compared are:

1. Iterative approach using a `forEach` loop (`excludedBanks.push(bank.name)`).
2. Functional programming approach using `filter()` and `map()` methods (`return banks.filter(bank => bank.excluded).map(bank => bank.name)`).

**Pros and Cons**

**Iterative Approach:**

* Pros:
	+ Easy to understand for developers familiar with traditional loops.
	+ May be more cache-friendly due to the explicit iteration.
* Cons:
	+ Can be slower than functional programming approaches due to the overhead of function calls.

**Functional Programming Approach:**

* Pros:
	+ Typically faster due to the use of built-in methods and optimized algorithms.
	+ More concise and expressive code, which can lead to better readability.
* Cons:
	+ May require more developer expertise familiar with functional programming concepts.
	+ Can be less intuitive for developers not familiar with this paradigm.

**Other Considerations**

* The test cases are running on a Chrome 120 browser on a Mac OS X 10.15.7 device, which may affect the results due to variations in hardware and software configurations.
* The `executionsPerSecond` metric is used to measure performance, which can be influenced by factors like CPU frequency, memory bandwidth, and cache performance.

**Alternatives**

Other approaches could include:

1. Using a more efficient data structure, such as a hash table or trie, to store the excluded banks.
2. Employing parallel processing techniques to speed up the iteration over large datasets.
3. Utilizing JavaScript's built-in `Array.prototype.reduce()` method to simplify the implementation.

Keep in mind that these alternatives might not necessarily lead to faster performance and should be carefully evaluated based on the specific requirements of the project.

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
collectAutomatedPricingExcludedBanks	23916964.0 Ops/sec
collectAutomatedPricingExcludedBanks2	20790872.0 Ops/sec

Related benchmarks:

testttttinnnnggg (version: 0)

Comparing performance of: collectAutomatedPricingExcludedBanks vs collectAutomatedPricingExcludedBanks2

Created: 10 months ago by: Guest

Jump to the latest result

collectAutomatedPricingExcludedBanks

collectAutomatedPricingExcludedBanks2

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):