Benchmark: IIFE assignment vs mutable assignment

Tests:

Mutable Assignment
function mut(i) { let b; if (i>0) { b = true } else { b = false } return b } mut(1)
IIFE assignment
function iife(i) { const b = (() => { if (i>0) { return true } else { return false } })() return b } iife(1)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Mutable Assignment
IIFE assignment

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (X11; Fedora; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36

Browser/OS: Chrome 107 on Fedora

View result in a separate tab

Test name	Executions per second
Mutable Assignment	788200000.0 Ops/sec
IIFE assignment	783884864.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its test cases.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark that compares two approaches for assigning values to variables: mutable assignments versus immediately invoked function expressions (IIFE) assignments.

In essence, the benchmark is testing how efficient these two approaches are in terms of execution speed. The variable `b` is assigned a value based on the input parameter `i`, and the result is returned.

**Options Compared**

The two options being compared are:

1. **Mutable Assignment**: This approach uses a traditional assignment operator (`=`) to assign the value of the expression to the variable `b`.
2. **IIFE (Immediately Invoked Function Expression) Assignment**: This approach uses an IIFE to encapsulate the code that assigns the value to `b`. The IIFE is invoked immediately after its definition, and it returns the value.

**Pros and Cons**

* **Mutable Assignment**:
	+ Pros: Simple and straightforward.
	+ Cons: May lead to variable hoisting issues if used in a larger scope.
* **IIFE Assignment**:
	+ Pros: Can help avoid variable hoisting issues due to its encapsulation nature.
	+ Cons: May introduce additional overhead due to the creation of a new function.

**Library and Purpose**

There are no external libraries being used in this benchmark. The code is self-contained and only relies on standard JavaScript features.

**Special JS Features or Syntax**

The benchmark uses a few special features, including:

* Immediately invoked function expressions (IIFE): `() => { ... }()`
* Variable hoisting: This feature is implicit in both approaches but may lead to issues if not carefully managed.
* Const declarations: Used to declare the constant variable `b`.

**Other Considerations**

When interpreting these results, consider the following:

* The benchmark only compares the execution speed of two specific approaches. In a larger context, you might need to consider other factors like memory usage or readability.
* The test cases are relatively simple and focus on basic assignment operations.

**Alternatives**

If you were to create an alternative benchmark for similar use cases, you could explore other approaches, such as:

1. **Arrow Functions**: Instead of IIFE, arrow functions (`() => { ... }`) might be used for a more concise code.
2. **Function Expressions with `let`**: This approach uses a function expression instead of an IIFE but still declares the variable using `let`.
3. **Object Literals**: Another alternative could involve using object literals to assign values, potentially leading to different optimization strategies.

By exploring these alternatives, you can gain insight into how various approaches might impact execution speed and other factors in your specific use case.

Related benchmarks:

IIFE assignment vs mutable assignment (version: 0)

Comparing performance of: Mutable Assignment vs IIFE assignment

Created: one year ago by: Guest

Jump to the latest result

Mutable Assignment

IIFE assignment

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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