Benchmark: includes + toLowerCase vs RegExp + i

Script Preparation code:

var str = "This is a simple test!";
var needle = "sImPle";

Tests:

lowercase + includes
str.toLowerCase().includes(needle.toLowerCase())
RegExp + i
new RegExp(needle, 'i').test(str);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lowercase + includes
RegExp + i

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 benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is defined by two test cases:

1. `lowercase + includes`: This test case measures the performance of using the `toLowerCase()` method followed by the `includes()` method to search for a substring within a string.
2. `RegExp + i`: This test case measures the performance of using a regular expression with the `i` flag (which makes the regex match in a case-insensitive manner) to search for a substring within a string.

**Options Compared**

In this benchmark, two approaches are being compared:

* Using the `toLowerCase()` method followed by `includes()`: This approach involves converting both the input string and the needle to lowercase before searching for the substring.
* Using a regular expression with the `i` flag: This approach involves creating a regex pattern that matches the substring in a case-insensitive manner.

**Pros and Cons of Each Approach**

1. **Using `toLowerCase()` method followed by `includes()`:**
	* Pros:
		+ Easy to implement and understand.
		+ Works well for simple cases where the input string is not too large.
	* Cons:
		+ Can be slower than using a regex pattern, especially for larger input strings.
		+ Requires two separate operations (converting to lowercase and searching), which can lead to overhead.
2. **Using a regular expression with the `i` flag:**
	* Pros:
		+ More efficient and flexible than the first approach, as it allows matching in a case-insensitive manner.
		+ Can handle more complex cases where the input string is large or contains special characters.
	* Cons:
		+ Requires knowledge of regex patterns, which can be intimidating for beginners.
		+ May not work well for all use cases (e.g., some libraries may not support regex flags).

**Library and Purpose**

There is no explicit library mentioned in the benchmark definition. However, it's likely that the `toLowerCase()` method and the regular expression pattern are part of the JavaScript standard library.

**Special JS Feature or Syntax**

Neither of the test cases uses a special JavaScript feature or syntax that requires explanation. Both approaches rely on basic string manipulation methods.

**Other Alternatives**

If you were to modify this benchmark, you might consider adding additional test cases, such as:

* Using the `i` flag with a regex pattern (e.g., `new RegExp(needle, 'gi')`) for case-insensitive matching.
* Adding performance metrics for different input sizes or complexity levels.
* Comparing the performance of these approaches using other browsers or platforms.

Keep in mind that the goal of this benchmark is to compare the performance of two approaches, so any additional test cases should aim to provide more comprehensive insights into each approach's strengths and weaknesses.

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
lowercase + includes	7375858.5 Ops/sec
RegExp + i	4179374.2 Ops/sec

Related benchmarks:

includes + toLowerCase vs RegExp + i (version: 0)

Comparing performance of: lowercase + includes vs RegExp + i

Created: 2 years ago by: Guest

Jump to the latest result

lowercase + includes

RegExp + i

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):