complexity-check

Analyzing code for time and space complexity involves breaking functions into loops, recursion, and data-structure operations to estimate Big-O scaling and identify performance bottlenecks. The analysis reports best, average, and worst-case complexity for individual functions or entire files.

Hidden quadratic behavior occurs when algorithms appear linear but execute with O(n^2) complexity due to nested data-structure operations or excessive copying. Detection involves analyzing I/O patterns and operation counts to flag high-risk scaling bottlenecks during code review.

Identifying N+1 queries and scaling bottlenecks requires detecting I/O-heavy loops and hidden quadratic patterns within functions. Code analysis flags these high-risk patterns and translates Big-O estimates into concrete operation counts for example input sizes to show practical impact.

Estimating concrete operation counts for example input sizes translates Big-O notation into real numbers and approximate timing. This helps teams judge the practical impact of algorithm complexity and understand scaling costs for specific data volumes during performance audits.

Code complexity analysis works for single functions, modules, and entire files. It estimates Big-O time and space complexity across the specified scope, detecting I/O patterns and providing developer-facing performance explanations for any level of code granularity.