What is cognitive complexity in software development?

It considers factors that can make code difficult to follow, such as:

• Nested loops
• Conditional statements
• Abrupt changes in logic flow
• Control flow
• Naming
• Code size

The aim of measuring cognitive complexity is to identify sections of code that may require simplification to improve readability and maintainability. This metric helps developers create cleaner, more intuitive code that can be easily understood by team members and future maintainers.

Cognitive complexity is calculated by assigning scores to various code structures that increase the mental effort required for understanding. Each structural element, such as loops, conditional statements, and nesting levels, adds to the complexity score. For example:

• A simple if-else statement might add one point
• A nested loop might add more points depending on its depth

The calculation also penalizes abrupt logic shifts, such as breaking out of loops or using goto statements. To measure the cognitive complexity of code, it is helpful to use static analysis tools, such as SonarQube, to automate this process. They analyze code and generate a cognitive complexity score that enables developers to identify problematic areas without time-consuming code reviews and discussions. Objective data reduces friction, and searches can be customized for internal processes.

In real-life software projects, cognitive complexity often arises in deeply nested logic, such as multiple levels of loops within conditional statements. Here are common instances of cognitive complexity in code:

The blocks may check multiple conditions but can become difficult to follow.

When they rely on a mix of base cases and recursive calls, they may confuse readers without clear documentation.

When used in long, chained expressions, these can also contribute to cognitive overload. Ternary operators are one example.

Developers encounter these issues in codebases that prioritize functionality over readability, often in time-constrained projects.

Reducing cognitive complexity involves writing code that is simple, modular, and easy to understand. The key is to take time to review code, reduce complex functions, use proper variable names, and add helpful comments. These and other steps include:

• Breaking large, complex functions into smaller, single-responsibility functions
• Flattening nested structures
• Reducing the levels of indentation in loops and conditionals
• Using meaningful variable names
• Adding comments where necessary
• Regularly refactoring code
• Adhering to coding standards

These best practices are crucial for maintaining low cognitive complexity and enable better collaboration and long-term code maintainability. To assist in creating quality code, developers can take advantage of code-related data, such as cycle time and pull-request lifetime, which can signal when code is too complex. Tools such as Enji offer features like Team Code Metrics that help monitor and analyze this data.