Benchmark: 5465484153219848987435487/

Tests:

Metodo 1
const targetString = 'abson' const mlLengthAnterior = 3 const str = 'son' if (targetString.indexOf(str) + mlLengthAnterior === targetString.length) {}
Metodo 2
const targetString = 'abson' const mlLengthAnterior = 3 const str = 'son' if (targetString.indexOf(str, targetString.length - mlLengthAnterior) !== -1) {}
Metodo 3
const targetString = 'abson' const mlLengthAnterior = 3 const str = 'son' if (targetString.slice(-mlLengthAnterior) === str) {}

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Metodo 1
Metodo 2
Metodo 3

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.

**Benchmark Overview**

The provided benchmark measures the performance of three different approaches for searching for an anagram of a given string in another string.

**Approaches Compared**

There are three test cases:

1. **Metodo 1**: Uses `indexOf()` method with two arguments, starting from the end of the original string.
2. **Metodo 2**: Uses `indexOf()` method with one argument and checks if the index is not equal to -1 (i.e., not found).
3. **Metodo 3**: Uses the `slice()` method to extract a substring from the beginning of the target string, matching it with the original string.

**Pros and Cons**

*   **Metodo 1**:
    *   Pros: Simple and straightforward implementation.
    *   Cons: May be slower due to the use of two arguments for `indexOf()`, which can lead to more iterations over the target string.
*   **Metodo 2**:
    *   Pros: Efficiently handles cases where the anagram is not found in the target string, as it immediately returns -1 without further checks.
    *   Cons: Requires checking the index value after calling `indexOf()`, which may add overhead for some use cases.
*   **Metodo 3**:
    *   Pros: Uses a built-in method (`slice()`), making it more efficient than manual substring creation or using `indexOf()` with two arguments.
    *   Cons: Requires extra memory allocation and handling of the resulting string slice, which can be slower for very large strings.

**Libraries Used**

None are explicitly mentioned in the provided benchmark definitions. However, some JavaScript engines might use internal libraries or optimizations that could affect the performance measurements.

**Special JS Features/Syntax**

None are used in these test cases.

**Other Considerations**

When implementing benchmarks like this one, it's essential to consider factors such as:

*   **Randomization**: Ensure that the input strings and their lengths are randomized to avoid any biases or predictable patterns.
*   **Multiple Iterations**: Run each benchmark multiple times with different inputs to account for variations in performance across executions.
*   **Platform and Browser Support**: Consider testing on various platforms, browsers, and devices to ensure a broader range of test results.

**Alternative Approaches**

Some alternative approaches that might be worth considering:

1.  Using regular expressions: This can provide a more concise way to search for anagrams but may incur additional overhead due to the complexity of the regex engine.
2.  Implementing a custom string searching algorithm: Depending on the specific requirements, a custom implementation might offer better performance or flexibility than using built-in methods.

These alternative approaches should be carefully evaluated in terms of their trade-offs and potential impact on performance.

By considering these factors and alternatives, you can gain a deeper understanding of how to design effective benchmarks that accurately reflect real-world use cases.

Related benchmarks:

5465484153219848987435487/ (version: 0)

Comparing performance of: Metodo 1 vs Metodo 2 vs Metodo 3

Created: 3 years ago by: Guest

Jump to the latest result

Metodo 1

Metodo 2

Metodo 3

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):