🚀 Smart DSA Toolkit – Debug Algorithms Like a Pro

A powerful toolkit to generate test cases, detect bugs, minimize failing inputs, and analyze algorithm performance.

⚡ 10-Second Demo

1. Compare your solutions and find the absolute smallest failing edge case:

npx dsa-gen compare brute.js optimized.js --preset=binary-search

Output:

❌ MISMATCH DETECTED
────────────────────────────
Input        : [2, 1, 2]
Expected     : 6
Got          : 5
────────────────────────────

🔍 Minimizing failing test case...
✅ Smallest failing case found!

❌ Minimal Failing Input
────────────────────────────
[2, 1, 2]
Expected     : 6
Got          : 5
────────────────────────────

Seed: 42
✅ Saved failing case to ./failing_case.json

❓ The Problem

• Debugging large inputs is hard: When your Leetcode or CP solution fails on a 10,000-element array, finding the exact issue is nearly impossible.
• Random test generators are dumb: Simple random outputs naturally miss critical edge cases (negatives, duplicates, boundaries).
• Benchmarking is tedious: Setting up performance.now() loops with varying N lengths gets repetitive.

✅ The Solution

This toolkit changes the game by:

1. Generating smart test cases mapped to algorithm constraints (e.g., sorting, binary search, graphs).
2. Finding bugs automatically by cross-testing your optimized code against a brute-force approach.
3. Minimizing failing cases (Delta-Debugging) automatically. Once it fails, it shrinks the array/string down to the absolute smallest reproducible failure.
4. Benchmarking performance gracefully to dynamically estimate Big-O complexities.

🔥 Features List

• 🧠 Smart test case generation (Arrays, Strings, Trees, Graphs, Matrices, Numbers)
• 🎲 Problem-aware edge cases built-in
• 🔬 Automatic minimizer (Killer Feature)
• ⚖️ Comparator (brute vs optimized tests)
• ⏱️ Benchmarking with O() complexity estimation
• 🪄 Interactive CLI wizard with colorful outputs (chalk & inquirer)
• 💾 Save / Load / Replay test suites
• 🎚️ Presets & Difficulty Levels (easy, medium, hard)

📦 Installation

Install globally to use the CLI anywhere:

npm install -g dsa-gen

Or install as a dev dependency to use it programmatically in your testing workflow:

npm install dsa-gen --save-dev

🛠 Usage Examples

CLI Command List

You can trigger the interactive wizard simply using:

npx dsa-gen

Otherwise, invoke tools directly:

• npx dsa-gen generate – Open test case generation wizard.
• npx dsa-gen replay – Replays standard ./failing_case.json config.
• npx dsa-gen compare – Trigger programmatic API info notice.
• npx dsa-gen benchmark – Trigger programmatic API info notice.
• npx dsa-gen visualize – Visualizes data structures as Ascii representations.

Programmatic Usage

1. Debugging with compareSolutions & Auto-Minimizer:

import { compareSolutions, generateArray } from 'dsa-gen';

const bruteForce = (arr) => { /* correct, slow logic */ };
const optimizedBuggy = (arr) => { /* fast logic w/ bug */ };

// Setup a generator using our difficulty presets
const generatorFn = () => [ generateArray({ size: 100, difficulty: 'hard' }) ];

compareSolutions(bruteForce, optimizedBuggy, {
  generatorFn,
  iterations: 1000,
  savePath: './failing_case.json', // Will export the minimal array here
  verbose: true
});

2. Educational Benchmarking runBenchmark:

import { runBenchmark, generateString } from 'dsa-gen';

const algorithm = (str) => { /* ... */ };
const generatorFn = (n) => [ generateString({ length: n }) ];

runBenchmark(algorithm, generatorFn, [10, 100, 1000, 10000]);

Output visually scales and automatically predicts O(N), O(N log N), O(1), or O(N^2).

🎯 Target Use Cases

• Debugging algorithms: Find the exact edge cases where an optimized logic fails.
• Interview preparation: Practice recognizing edge cases and testing tree/graph manipulations.
• Competitive programming: Speed up testing locally before pushing to platforms like Codeforces.
• Performance testing: Quickly assert O(N) constraints before submission.