Software Engineering basics - Big-O Notation

Q: What is Big-O Notation in Software Engineering?

A:
Big-O Notation is a way to measure the efficiency of an algorithm by analyzing how its resource usage grows as the size of the input increases. It helps developers evaluate both time complexity (how fast an algorithm runs) and space complexity (how much memory it uses). Instead of focusing on exact execution time, Big-O expresses performance in terms of input size n, giving a high-level view of scalability.

For example, if an algorithm requires doubling the number of operations every time the input size doubles, it may be described as O(n²) (quadratic time). On the other hand, an algorithm that only requires a fixed number of steps regardless of input size would be O(1) (constant time). Common complexities include:

• O(1): Constant time – performance does not depend on input size.

• O(log n): Logarithmic time – efficient growth, like binary search.

• O(n): Linear time – work grows proportionally with input size.

• O(n²): Quadratic time – slower, common in nested loops.

By understanding Big-O, software engineers can compare algorithms and choose the most efficient one for large-scale problems. For instance, using a sorting algorithm like Merge Sort (O(n log n)) is far more efficient than Bubble Sort (O(n²)) when working with thousands or millions of data items.