How to evaluate the complexity of binary code…

Assessing the complexity of binary code is not a standard task for a Frontend developer. Usually, this is done by performance analysts, reverse engineers, or security specialists.

However, hypothetically assuming that we are talking about analyzing the performance of code (for example, WebAssembly, which is a binary format), the approaches could be as follows:

Static analysis: Studying the code structure without executing it. It allows estimating:
- Code size.
- Number of instructions.
- Register usage.
- Stack depth.
- Presence of loops and recursion (with limited accuracy).
Dynamic analysis: Executing the code and collecting metrics. It allows estimating:
- Execution time.
- CPU load.
- Memory usage.
- Behavior under different input data. Profilers can be used.
Control Flow Graph (CFG) analysis: Visualizing possible execution paths of the code. Helps identify complex branching and loops.
Data Dependency Analysis: Determining how data is passed between instructions. Helps identify bottlenecks in data processing.
Using specialized tools: There are tools for reverse engineering and binary code analysis (e.g., Ghidra, IDA Pro), but their use goes beyond typical Frontend developer tasks.

For a Frontend developer, a more relevant task is estimating the complexity of JavaScript or other source code, which often is based on:

Time Complexity: How execution time grows with increasing input size (e.g., O(1), O(n), O(n log n), O(n^2)).
Space Complexity: How memory consumption grows with increasing input size.

Time complexity assessment is usually performed by analyzing the algorithm. Popular notation examples include:

Notation	Name	Description	Example operation
O(1)	Constant	Execution time does not depend on input data	Accessing an array element by index
O(log n)	Logarithmic	Execution time grows slowly	Binary search
O(n)	Linear	Execution time grows proportionally	Linear search
O(n$^2$)	Quadratic	Execution time grows quickly	Bubble sort

An example of estimating time complexity in JavaScript:

// O(n) - linear complexity
function sumArray(arr) {
  let sum = 0;
  for (let i = 0; i < arr.length; i++) {
    sum += arr[i];
  }
  return sum;
}

// O(n^2) - quadratic complexity
function multiplyMatrices(matrixA, matrixB) {
  const rowsA = matrixA.length;
  const colsA = matrixA[0].length;
  const rowsB = matrixB.length;
  const colsB = matrixB[0].length;
  if (colsA !== rowsB) {
    throw new Error("Incompatible matrix sizes");
  }
  const result = new Array(rowsA).fill(0).map(() => new Array(colsB).fill(0));

  for (let i = 0; i < rowsA; i++) {
    for (let j = 0; j < colsB; j++) {
      for (let k = 0; k < colsA; k++) {
        result[i][j] += matrixA[i][k] * matrixB[k][j];
      }
    }
  }
  return result;
}

Thus, assessing the complexity of binary code is a task beyond the scope of standard Frontend developer responsibilities, unlike assessing the complexity of source code, which is an important aspect of developing performant web applications.

How to evaluate the complexity of binary code?