What is Cyclomatic Complexity?

Introduced by Thomas McCabe, cyclomatic complexity counts the decision points in code (if, while, for, case, and logical operators) to estimate how many distinct paths run through it. A simple formula is complexity = number of decision points + 1. A function with no branches has complexity 1; each added decision increases it. Higher numbers mean more paths, more ways to go wrong, and more tests needed to exercise them.

For testers, the metric is doubly useful. First, it signals risk: highly complex functions are harder to understand, more defect-prone, and harder to maintain, so they are good candidates for extra testing, review, or refactoring. Second, it informs coverage: the cyclomatic complexity number is the minimum number of test cases needed to achieve full branch (basis path) coverage of that code, so it directly relates to how much testing a function requires.

It is part of static analysis and maintainability assessment, not something you run the software to measure. Teams often set thresholds (for example, flag functions above 10) to catch complexity creep early. The metric does not capture everything (it ignores data complexity and readability), so it is a useful signal among several, not a sole measure of quality.