AI Big-O Complexity Analyzer
Analyze the time and space complexity of your code. Our AI-powered Big-O analyzer provides detailed complexity analysis with explanations.
Features
- Time Complexity: Big-O notation for execution time
- Space Complexity: Memory usage analysis
- Detailed Explanation: Understand how complexity is derived
- Optimization Tips: Suggestions for improvement
How to Use
- Select the programming language
- Paste the code you want to analyze
- Click Generate to get complexity analysis
- Review the Big-O notation and explanation
What Gets Analyzed
- Loops: Single, nested, and multiple loops
- Recursion: Recursive function calls
- Data Structures: Array, list, tree, graph operations
- Algorithm Patterns: Sorting, searching, traversal
Common Complexity Classes
| Notation | Name | Example |
|---|---|---|
| O(1) | Constant | Array access by index |
| O(log n) | Logarithmic | Binary search |
| O(n) | Linear | Simple iteration |
| O(n log n) | Linearithmic | Merge sort |
| O(n²) | Quadratic | Nested loops |
| O(2ⁿ) | Exponential | Recursive Fibonacci |
Analysis Includes
- Best case complexity
- Average case complexity
- Worst case complexity
- Space complexity
- Auxiliary space usage
Supported Languages
- Python
- JavaScript / TypeScript
- Java
- C / C++
- Go
- Ruby
- And many more
Tips for Optimization
The analyzer also provides suggestions for:
- Reducing nested loops
- Using more efficient data structures
- Applying memoization
- Choosing better algorithms