Benchmark: Test Optimisation

Script Preparation code:

var a = [{
    subChallenges: [{
        state: "ACCEPTED"
    }, {
        state: false
    }, {
        state: "ACCEPTED"
    }]
}, {
    subChallenges: [{
        state: "ACCEPTED"
    }, {
        state: false
    }, {
        state: false
    }]
}, {
    subChallenges: [{
        state: "ACCEPTED"
    }, {
        state: false
    }, {
        state: false
    }]
}, {
    subChallenges: [{
        state: "ACCEPTED"
    }, {
        state: "ACCEPTED"
    }, {
        state: "ACCEPTED"
    }]
}]

function Function_0(challenges) {
    let count = 0;
    if (challenges) {
        challenges.map((challenge) => {
            if (
                challenge.subChallenges &&
                challenge.subChallenges.filter(
                    (subChallenge) => subChallenge.state !== "ACCEPTED"
                ).length === 0
            )
                count++;
        });
    }
    return count;
}

function Function_1(challenges) {
    let count = 0;
    if (challenges) {
        let i = challenges.length;

        while (i--) {
            let challenge = challenges[i];

            if (!challenge.subChallenges) {
                continue;
            }

            let j = challenge.subChallenges.length,
                truthy = true;

            while (j--) {
                if (challenge.subChallenges.state !== "ACCEPTED") {
                    truthy = false;
                  	continue;
                }
            }

            if (truthy) {
                count++
            }
        }
    }

    return count;
}

Tests:

Original
Function_0(a);
Opti
Function_1(a);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Original
Opti

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's dive into the benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark defines two functions: `Function_0` and `Function_1`. Both functions take an array of objects as input (`challenges`) and return a count of sub-challenges with a specific state.

**Function_0**

This function iterates over the `subChallenges` array using `Array.prototype.map()` and checks if there are any sub-challenges with a non-accepted state. If so, it increments a counter.

**Function_1**

This function uses a while loop to iterate over the `challenges` array from the end to the beginning (using the `i--` loop variable). For each challenge, it checks the length of its `subChallenges` array and sets a flag (`truthy`) to false if any sub-challenge has an accepted state. If all sub-challenges have accepted states, it increments a counter.

**Comparison**

The two functions are compared in terms of performance. The benchmark is executed multiple times for each function, and the execution time is recorded.

**Pros and Cons**

* **Function_0**
	+ Pros:
		- Easy to read and understand
		- Uses a well-known JavaScript method (`Array.prototype.map()`)
	+ Cons:
		- May be slower due to the overhead of `map()` (in this case, it's unlikely to be a significant difference)
* **Function_1**
	+ Pros:
		- More efficient in terms of memory usage (no unnecessary array creations or string concatenations)
	+ Cons:
		- Less readable and maintainable due to the use of while loops and manual indexing
		- May be slower due to the overhead of loop variables and conditional statements

**Library**

The benchmark uses no external libraries beyond the standard JavaScript library.

**Special JS Features/Syntax**

* None explicitly mentioned, but `Array.prototype.map()` is used in both functions. This is a standard JavaScript method that's widely supported across browsers.
* The use of `i--` in `Function_1` is an older syntax that's still supported by modern browsers.

**Alternatives**

Other ways to compare the performance of these two functions might include:

* Using a more concise or expressive language (e.g., using `Array.prototype.filter()` instead of `map()`)
* Adding additional logic or features to one of the functions to make it more representative of real-world use cases
* Using different data sets or scenarios to test the functions' performance in different contexts

Related benchmarks:

Test Optimisation (version: 0)

Comparing performance of: Original vs Opti

Created: 4 years ago by: Guest

Jump to the latest result

Original

Opti

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):