Big-O Complexity Benchmarker

Asymptotic complexity is typically taught via pure formulas without physical performance references. Learners find it hard to understand why O(N log N) sorting is exponentially better than O(N^2) loops at scale, or how fast O(2^N) recursive steps starve CPU resources.

This project makes complexity visible by profiling execution times and graphing results dynamically, bridging theory and runtime.

The benchmark runner operates as a localized performance loop:

1. User loads preset scripts or custom loops.
2. Execution loop runs with varying input sizes N = [10, 50, 100, 500].
3. High-resolution time-stamps capture start and end execution offsets.
4. Data coordinates are compiled and plotted onto the Canvas line grid.

The benchmark compares standard search and sort algorithms:

• JavaScript/TypeScript execution step profiling
• Microsecond-level performance benchmarking API bounds
• Dynamic chart drawing via HTML5 Canvas coordinates
• Time-space scaling tradeoff analysis
• Asymptotic curves comparisons

* **Time Complexity**: Profiler calculations run in O(1) time-step recording. Graphing runs in O(N) plotting time.
* **Space Complexity**: O(N) storage to maintain performance data arrays before rendering.

• Add support for space complexity heap profile tests.
• Add multi-thread execution comparisons using Web Workers.
• Integrate automated complexity formula estimations from AST.