Benchmark: Adding fields to functions

Tests:

eval-set-name
var f; var sum = 0; for(var i = 0; i <= 100000; i += 1) { f = function(j) { return j; }; f.name = "f" + i; sum += f(i); } console.log(f.name);
no-eval-native-name
var f; var sum = 0; for(var i = 0; i <= 100000; i += 1) { f = function f(j) { return j; }; sum += f(i); } console.log(f.name);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
eval-set-name
no-eval-native-name

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided JSON and explain what's being tested, compared, and their pros/cons.

**Benchmark Definition**

The benchmark definition is empty, which means that the user doesn't provide any specific code to be executed. Instead, two test cases are provided:

1. `eval-set-name`: This test case executes a loop 100,000 times, where in each iteration:
	* A function `f` is assigned and its name is set using the `eval()` function.
	* The function `f` takes an argument `j` and returns it.
	* The function's name is then used to access the function (e.g., `f(i)`).
2. `no-eval-native-name`: This test case executes a loop 100,000 times, where in each iteration:
	* A function `f` is assigned and its name is set using a regular expression.
	* The function `f` takes an argument `j` and returns it.

**Comparison**

The two test cases are being compared to see which approach performs better:

1. Using `eval()` to set the function's name (`eval-set-name`).
2. Setting the function's name without using `eval()` (`no-eval-native-name`).

**Pros and Cons**

**eval-set-name**

Pros:

* Easy to implement, as it only requires a single line of code.
* Fast execution, as it doesn't incur any overhead from parsing or compiling the code.

Cons:

* Potentially slower performance due to the use of `eval()`.
* May introduce security vulnerabilities if used with untrusted input.

**no-eval-native-name**

Pros:

* More efficient than using `eval()` for large datasets, as it avoids the overhead of parsing and compiling the code.
* Safer than using `eval()`, as it doesn't execute arbitrary code.

Cons:

* Requires more complex implementation, as it involves regular expressions to set the function's name.
* May be slower than `eval-set-name` due to the overhead from regular expression matching.

**Library: eval**

The `eval()` function is a built-in JavaScript function that parses and executes a string as JavaScript code. It's used in both test cases to set the function's name. The use of `eval()` can introduce security vulnerabilities if used with untrusted input, so it's generally recommended to avoid using it whenever possible.

**Other Considerations**

* Both test cases execute the same loop 100,000 times, which means that any performance difference will be dominated by the overhead from setting and accessing the function's name.
* The use of `eval()` in one test case may introduce a significant performance penalty compared to the other test case.
* Regular expressions are used to set the function's name in the `no-eval-native-name` test case, which can add complexity and potential performance overhead.

**Alternatives**

There are alternative approaches that could be used to set the function's name without using `eval()` or regular expressions. For example:

1. Using a global variable with a specific prefix (e.g., `_func_`) to differentiate between functions.
2. Utilizing a library like Lodash or Underscore.js, which provide utility functions for working with functions and variables.

However, these alternatives may add complexity and overhead compared to the simple approach used in `eval-set-name`.

Related benchmarks:

Adding fields to functions (version: 0)

Comparing performance of: eval-set-name vs no-eval-native-name

Created: 9 years ago by: Guest

Jump to the latest result

eval-set-name

no-eval-native-name

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):