Benchmark: Comparing two string difference methods, one using LCS - Longest Common Subsequence

Tests:

Using LCS
function diff(a, b) { const lcsMatrix = Array(b.length + 1).fill(null).map(() => Array(a.length + 1).fill(null)); for (let i = 0; i <= a.length; i++) { for (let j = 0; j <= b.length; j++) { if (i === 0 || j === 0) { lcsMatrix[i][j] = 0; } else if (a[i - 1] === b[j - 1]) { lcsMatrix[i][j] = lcsMatrix[i - 1][j - 1] + 1; } else { lcsMatrix[i][j] = Math.max(lcsMatrix[i - 1][j], lcsMatrix[i][j - 1]); } } } let i = a.length, j = b.length; const diffs = []; while (i > 0 && j > 0) { if (a[i - 1] === b[j - 1]) { i--; j--; } else if (lcsMatrix[i - 1][j] > lcsMatrix[i][j - 1]) { diffs.unshift(['-', i - 1, a[i - 1]]); i--; } else { diffs.unshift(['+', i, b[j - 1]]); j--; } } while (j > 0) { diffs.unshift(['+', i, b[j - 1]]); j--; i--; } return diffs; } diff("hello","hello world")
Generic
const diff = (a, b) => [...a].map((c, i) => c !== b[i] && [i, b[i] || '']).filter(Boolean); diff("hello","hello world")

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Using LCS
Generic

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Purpose**
The purpose of this benchmark is to compare two string difference methods: one using Longest Common Subsequence (LCS) algorithm and another generic approach. The goal is to determine which method is faster for comparing two strings.

**Options Compared**

1. **Using LCS**: This method uses the Longest Common Subsequence algorithm to find the differences between two strings. It creates a matrix to store the lengths of common subsequences and then uses this information to construct the list of differences.
2. **Generic**: This method uses a simple array-based approach to compare two strings. It iterates over each character in both strings, checks for equality, and if not, adds an index and the corresponding character from one string.

**Pros and Cons**

1. **Using LCS**:
	* Pros: Efficient for finding exact matches between substrings.
	* Cons: Can be slower due to matrix calculation and iteration.
2. **Generic**:
	* Pros: Simple and fast implementation.
	* Cons: May not be efficient for finding exact matches between substrings.

**Library/Function Used**
In the LCS method, no specific library or function is used beyond basic array operations. However, the `diff` function in both methods uses a simple filter operation (`filter(Boolean)`).

**Special JS Feature/Syntax**
The benchmark does not use any special JavaScript features or syntax beyond standard array and string operations.

**Other Alternatives**

1. **Levenshtein Distance**: Another algorithm for finding differences between strings, which could be used as an alternative to LCS.
2. **Regular Expressions**: Regular expressions can be used to find differences between strings, but they might add unnecessary overhead due to the complexity of regular expression engines.

**Benchmark Preparation Code Analysis**
The benchmark preparation code is empty in both test cases, indicating that the focus is on comparing the execution times of different string difference methods rather than preparing some external data or conditions.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36

Browser/OS: Chrome 107 on Windows

View result in a separate tab

Test name	Executions per second
Using LCS	58188.4 Ops/sec
Generic	2707842.8 Ops/sec

Related benchmarks:

Comparing two string difference methods, one using LCS - Longest Common Subsequence (version: 0)

Comparing performance of: Using LCS vs Generic

Created: one year ago by: Guest

Jump to the latest result

Using LCS

Generic

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):