Benchmark: Test if an extension is included in the list of candidates

Tests:

RegExp.prototype.test()
/^\.(?:js|mjs|cjs|jsx|ts|mts|cts|tsx)$/.test('.js')
Array.prototype.includes()
['.js', '.mjs', '.cjs', '.jsx', '.ts', '.mts', '.cts', '.tsx'].includes('.js')

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
RegExp.prototype.test()
Array.prototype.includes()

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 benchmark definition and test cases to explain what's being tested.

**Benchmark Definition**

The provided JSON defines two benchmark definitions:

1. `/^\\.(?:js|mjs|cjs|jsx|ts|mts|cts|tsx)$/.test('.js')`
2. `['.js', '.mjs', '.cjs', '.jsx', '.ts', '.mts', '.cts', '.tsx'].includes('.js')`

Both benchmarks test the performance of JavaScript (JS) files, but they use different approaches.

**Approaches**

1. **Regular Expressions**: The first benchmark uses a regular expression (`/^\\.(?:js|mjs|cjs|jsx|ts|mts|cts|tsx)$/.test('.js')`) to check if the string '.js' matches the pattern of any JavaScript file extension (including variants like `mjs`, `cjs`, etc.). This approach is straightforward but may be slower due to the overhead of regular expression evaluation.
2. **String Array Search**: The second benchmark uses a simple array search (`['.js', '.mjs', '.cjs', '.jsx', '.ts', '.mts', '.cts', '.tsx'].includes('.js')`) to check if the string '.js' is present in the list of JavaScript file extensions. This approach is likely faster since it uses a direct lookup, but it may not be as robust as the regular expression approach.

**Pros and Cons**

* **Regular Expressions**:
	+ Pros: Robustness (matches various file extensions), ease of maintenance (can add or remove variants easily).
	+ Cons: Slower execution due to regular expression overhead.
* **String Array Search**:
	+ Pros: Faster execution, simple implementation.
	+ Cons: Limited robustness (only checks for exact string presence).

**Library and Special JS Features**

Neither benchmark uses a specific library. However, the use of ES6+ syntax features like template literals (`".js"`) is evident in the `String.prototype.includes()` method.

**Other Alternatives**

If you want to optimize the first benchmark, you could consider using a more efficient regular expression engine or precompiling the regular expressions for each JavaScript file extension variant. For the second benchmark, using a faster string matching algorithm like Boyer-Moore or Knuth-Morris-Pratt might provide a performance boost.

Keep in mind that these optimizations are implementation-specific and may not be relevant to the current benchmark setup.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36

Browser/OS: Chrome 109 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
RegExp.prototype.test()	42704028.0 Ops/sec
Array.prototype.includes()	629719680.0 Ops/sec

Related benchmarks:

Test if an extension is included in the list of candidates (version: 0)

Comparing performance of: RegExp.prototype.test() vs Array.prototype.includes()

Created: one year ago by: Guest

Jump to the latest result

RegExp.prototype.test()

Array.prototype.includes()

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):