Time Complexity Estimator

Time Complexity is a mathematical representation of the amount of time an algorithm takes to run as a function of its input size (n). Big-O notation (e.g., O(n), O(log n)) is the standard language used to describe the "worst-case scenario" for an algorithm's performance, helping developers identify how well their code will scale as data grows.

Our tool uses Static Code Analysis. It tokenizes your source code and traverses the structure to identify loops, conditional branching, and recursive calls. By calculating the nesting depth and control flow patterns, it can estimate complexity based on proven mathematical rules without the risk of infinite loops or crashes during execution.

We officially support JavaScript, Python, Java, and C++. The engine is also capable of analyzing Pseudocode as long as it follows standard algorithmic structures. Each language has custom tokenizer rules to accurately identify syntax specific to loops and function calls.

Yes. If your function calls itself, our engine identifies it as recursive. It specifically looks for patterns like binary division (n/2) to identify Divide & Conquer algorithms, applying principles like the Master Theorem to provide results such as O(n log n) or O(2ⁿ).

While Time Complexity focuses on how long an algorithm runs, Space Complexity looks at how much memory (RAM) it consumes. Our tool identifies array initializations, data structure scaling, and recursive stack frames to give you a complete picture of your algorithm's resource footprint.

Max Loop Depth represents the highest number of nested loops in your code. For example, a loop inside another loop has a depth of 2 (O(n²)). High nesting depth often suggests that the algorithm can be optimized by flattening the logic or using better data structures like Hash Maps.

Absolutely. For platforms like LeetCode or Codeforces, staying within the time limit is critical. Our analyzer helps you quickly verify if your O(n²) approach will pass for n=10⁵ inputs (it won't!) before you even hit submit, saving you valuable time during contests or technical interviews.

Yes. Our static analyzer recognizes standard library methods that contribute to complexity. For instance, in JavaScript, calling .sort() adds O(n log n) overhead, while a .map() inside a for loop triggers O(n²) analysis. This catches "hidden" complexities that manual counting often misses.

100% Yes. All analysis is performed client-side within your browser. Your code never leaves your machine, making it perfectly safe for analyzing proprietary or sensitive algorithms without worrying about data leaks.