Cyclomatic Complexity Calculator
Use our cyclomatic complexity calculator to calculate cyclomatic complexity instantly. Includes the cyclomatic complexity definition, meaning, and formula, plus cyclomatic complexity explained with an example, guidance on how to reduce cyclomatic complexity, and a quick note on cyclomatic complexity vs cognitive complexity.
What is Cyclomatic Complexity?
Cyclomatic complexity is a software metric that measures the number of independent paths through a program’s source code. In plain terms, it estimates how complex a function or method is based on its branching and decision points.
If you’re looking for cyclomatic complexity meaning or cyclomatic complexity explained, the idea is: more branches (if/else, switch cases, loops, logical conditions) typically means more paths to test, higher maintenance cost, and a greater risk of bugs.
Cyclomatic complexity is widely used as a cyclomatic complexity metric in static analysis tools. This cyclomatic complexity calculator helps you calculate cyclomatic complexity from the number of decision points (and optionally edges/nodes) using the standard formula.
Cyclomatic Complexity Formula
The classic cyclomatic complexity formula (McCabe) uses control flow graph values, but it can also be computed as 1 + number of decision points for a single connected function.
E = edges, N = nodes, P = connected components (usually P = 1 for a single function).
D = number of decision points (if/else-if, while, for, case, catch, &&, || depending on rules).
If a function has 4 decision points, cyclomatic complexity is 5 (five independent paths).
Higher cyclomatic complexity generally means more cases to test and higher maintenance effort.
How to Calculate Cyclomatic Complexity
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Count decision points (D), such as if/else-if, switch cases, loops, and conditional branches in the function.
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Use the shortcut formula M = D + 1 to calculate cyclomatic complexity for a single function.
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If you’re using control flow graph values, enter edges (E), nodes (N), and components (P) and compute M = E - N + 2P.
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Review the result and compare it to common thresholds for what is a good cyclomatic complexity.
Frequently Asked Questions
Cyclomatic complexity is a code complexity metric that measures the number of independent execution paths through a program or function.
It quantifies how many distinct paths exist due to branches and decisions, often used to estimate test effort and maintainability.
Use M = E - N + 2P (McCabe). For a single connected function, a common shortcut is M = D + 1, where D is decision points.
Count decisions (if/else-if, loops, switch cases, ternaries, and sometimes logical operators like && and || depending on your tool) and compute M = decisions + 1.
Lower is generally better. Many teams treat values around 1–10 as manageable, while higher values may signal refactoring needs (exact thresholds vary by team/tool).
Break large functions into smaller ones, replace deep nested conditionals with early returns, use polymorphism/strategy patterns instead of large switch blocks, and simplify boolean logic.
Cyclomatic complexity counts independent paths; cognitive complexity focuses on how hard code is to understand (nesting and mental effort). A function can have similar cyclomatic complexity but very different cognitive complexity.
Yes—this cyclomatic complexity calculator computes the metric from decision points or from control flow graph inputs.