How to Identify and Reduce Cognitive Complexity in Your Codebase

You’ve worked with code that feels effortless to understand – everything flows, and you know exactly what it does. But then, there’s the other kind – the kind that slows you down, makes you second-guess yourself, and forces you to untangle confusing logic just to make a simple change.

This is frustrating and impacts your productivity, your team, and the overall quality of your work. Cognitive complexity determines how hard code is to read and modify.

By the end of this article, you’ll understand the definition of cognitive complexity, what causes it, and how to improve code readability for a smoother workflow. Let's start!

What Is Cognitive Complexity?

Cognitive complexity measures how difficult it is for you to understand code based on its structure and logic. Unlike other metrics, it focuses on how much effort you need to follow the code. Complex logic, nesting, and excessive statements contribute to higher cognitive complexity. 

Cognitive Complexity vs. Cyclomatic Complexity

Cognitive complexity measures how hard it is for you to read and understand code. Cyclomatic complexity, on the other hand, counts the number of execution paths within a function.

While both indicate complexity, they measure different aspects. Cyclomatic complexity focuses on testability, while cognitive complexity looks at readability and maintainability.

Understanding how these metrics impact real-world development is crucial, so researchers analyzed their relationship with code comprehension.

Research involving 216 junior developers assessed 12 Java classes with different cognitive and cyclomatic complexity levels. The findings showed that cognitive and cyclomatic complexity are modest predictors of code understandability, but they can’t be used to assess problem severity.

Despite the study not showing stronger correlations, cognitive complexity isn’t good. That brings us to the next point:

Why Avoid Cognitive Complexity?

Cognitive complexity should be avoided because it makes complex code harder to read, modify, and debug. When there is complexity in software engineering, systems are rarely straightforward.

According to the Cynefin framework, software falls into the "complex" category, which means you can't know every detail at once. Instead, you need to break problems into smaller parts for better understanding. When cognitive complexity is high, here are the challenges you face:

• More bugs and technical debt
• More rework needed
• A higher learning curve for new developers

Keeping code simple, reducing nesting levels, and using clear logic allows you to make development faster, smoother, and easier to maintain. You need less cognitive complexity to maintain high code quality.

Cognitive Complexity Causes

When cognitive complexity increases, it leads to mistakes and slower development. Several factors contribute to this, and identifying these issues helps you improve code maintainability and reduce confusion. Here are some of the causes.

Cyclomatic Complexity

Too many conditional statements, if-else, loops, and logical operators can make a function overwhelming. When you have multiple nesting levels, the effort required to understand the flow skyrockets.

For example, imagine this function:

This deep nesting forces you to track multiple conditions at once. Instead, you should simplify decision points by using early returns or breaking logic into smaller functions, because now it will be easier to read and modify.

Poor Naming

When variable, function, or class names don’t describe their purpose, you spend extra time figuring out what the code does. Poor naming increases program comprehension difficulty and slows down debugging.

A survey of 57 research scientists revealed that 58% lacked formal instruction in writing readable code. Among the challenges faced, poor naming conventions were highlighted as a significant barrier to code readability and comprehension.

Let's compare these two examples:

❌ x.processData() - What is "x"? What data is being processed?
✅ invoice.calculateTotalAmount() - Clearly defines what the function does.

To improve readability, you should use descriptive names that reflect intent. Follow consistent naming conventions, and avoid abbreviations that require unnecessary interpretation. This makes your code base more intuitive for both you and your team.

Poor Architecture

A lack of modular design results in tangled dependencies, which makes small changes risky and time-consuming. When components are tightly coupled, modifying one function can unexpectedly break others.

For instance, your payment processing logic could be embedded in your user management module in a monolithic structure. This makes changes to payments that could unintentionally affect authentication.

Following effective strategies like the SOLID principles helps reduce complexity. Breaking code into independent modules allows you to modify one part without affecting the rest, improving long-term flexibility.

Large Functions or Classes

When a function stretches over hundreds of lines, it becomes nearly impossible to understand at a glance. The more logic packed into a single function, the harder it is to track execution flow and debug issues.

Take this example:

Instead of writing one massive function, you should break it down into smaller, reusable methods. The single responsibility principle helps you keep functions focused on one task and makes them easier to read, test, and maintain.

High Essential Complexity

Some tasks are naturally complex, but you can still structure them to make them easier to manage. Essential complexity comes from problems that are difficult by nature, such as advanced mathematical calculations or intricate business logic. 

In contrast, accidental complexity arises from how we implement solutions, like poor code structure, unnecessary dependencies, or unclear naming. While essential complexity is unavoidable, accidental complexity is introduced by us and can (and should) be minimized.

So while you can’t simplify the problem itself, you can break it down into smaller, manageable parts.

For example, instead of writing a massive function that calculates multiple financial metrics simultaneously, you should separate tax calculations, interest rates, and payment schedules into distinct functions.

This keeps your source code organized and prevents unnecessary confusion. Using clear abstractions, well-defined modules, and meaningful function names is how you avoid adding accidental complexity that slows you down.

Not Following Conventions

Ignoring established coding conventions leads to inconsistency, which makes your code block harder to read and maintain. When different team members use different styles, the structure of your project becomes unpredictable and increases cognitive load.

For instance, mixing naming styles like camelCase, snake_case, and PascalCase in the same codebase forces you to adjust to different patterns constantly. Instead, following style guides like PEP8 for Python or the Airbnb style guide for JavaScript creates uniformity.

Linting tools such as ESLint, Prettier, and SonarQube help you automatically check for inconsistencies to ensure that your team follows best practices. Keeping code structured and predictable reduces complexity and lets you focus on functionality rather than deciphering formatting issues.

Coding becomes significantly easier in terms of complexity when clear standards and linting tools are in place to enforce consistency and best practices.

New Codebase

Working with a new codebase can be overwhelming, especially if it has little documentation or a clear structure. You have to deal with unfamiliar logic, understand how different modules interact, and figure out hidden dependencies, all of which increase cognitive complexity.

A survey by Sourcegraph revealed that 94% of software development professionals are affected by the increasing volume and complexity of codebases.

Imagine joining a team where the project consists of thousands of lines of code written over the years without proper documentation. Without guidance, you might struggle understanding how different functions connect, leading to frustration and mistakes.

Teams should provide onboarding guides, internal wikis, and structured code reviews to ease this transition. Pair programming and mentoring can also help you get familiar with the system faster. Reviewing previous pull requests gives you good insights into coding standards and decision-making processes, while asking questions during team discussions.

New Domain

Switching to a new industry brings extra challenges. Even if you’re an experienced developer, business logic in healthcare, finance, or cybersecurity has unique rules and requirements that add complexity.

For example, if you move from e-commerce to fintech, you suddenly deal with strict compliance laws, complex transaction handling, and risk management protocols. Without a clear understanding of these concepts, your code comprehension suffers, making even simple tasks feel overwhelming.

In domain-driven design (DDD), defining clear context boundaries is essential. Overlapping or poorly defined boundaries create confusion and redundancy. To reduce this, you should work closely with domain experts, use precise terminology, and document key business rules.

How to Measure Cognitive Complexity

You need clear metrics highlighting problem areas in your code to reduce cognitive complexity. These measurements help you assess your code, predict risks, and prioritize refactoring. Tracking complexity allows you to write more maintainable code and improve overall readability.

1. Cognitive Complexity Score

The cognitive complexity score measures how difficult your code is to read and understand. Unlike cyclomatic complexity, it considers nesting levels and unintuitive logic, which makes it a better indicator of human readability. Its cognitive complexity increases if your code has deeply nested structures or hard-to-follow logic.

For example, a function with multiple layers of conditions is harder to follow than one with a clear linear flow. Tools like SonarQube can help you analyze cognitive complexity and identify sections that need simplification. Using these insights lets you restructure your code to make it easier for you and your team to work with.

2. Cyclomatic Complexity (CC)

Cyclomatic complexity measures the number of independent paths through your code. The higher the score, the harder your code is to test and maintain. If a function contains too many branching conditions, it becomes more challenging to debug and modify.

Thresholds for CC:

• 1-10: Simple, easy to test
• 11-20: Moderate complexity
• 20+: High complexity (needs refactoring)

For example, a function with multiple nested loops and conditional statements will have a high CC score, which signals that it may be challenging to manage. Tools like SonarQube, ESLint, and PMD can help you analyze these values.

While cyclomatic complexity measures execution paths, SonarQube’s cognitive complexity metric highlights sections that are hard to read. Refactoring functions with excessive nesting, such as breaking them into smaller, more modular components, can improve both metrics, leading to better maintainability.

3. Halstead Complexity Measures

Halstead complexity focuses on how readable and maintainable your code is based on operators and operands. It provides a detailed breakdown of how much effort it takes to understand and modify a function.

Key metrics include (but are not limited to):

• Volume: The total size of the code
• Effort: The difficulty of reading and working with the code
• Testing time: The estimated time needed to test the code

For example, a function overloaded with complex operations and unnecessary variables will have a high Halstead complexity score, indicating that it requires simplification. Tools like Radon and CodeMR can help you measure and improve these values.

4. Maintainability Index

The maintainability index determines how easy it is to update and manage your code is. A lower score means the code is harder to maintain, which increases technical debt and slows down development.

Thresholds for maintainability:

• 0-9: High-risk code, difficult to update
• 10-19: Moderate maintainability
• 20-100: Easy to maintain

For example, if a function spans hundreds of lines with no clear structure, it will receive a low maintainability score. Tools like Visual Studio Code Analysis and SonarQube can help you track this metric to ensure your code stays manageable over time.

5. Pull Request (PR) Size

When a pull request is too large, it becomes harder for reviewers to understand the changes. A PR with hundreds of modified lines of code increases cognitive load and makes it difficult to spot issues. Large PRs also slow your review process and increase the risk of bugs slipping through.

For better peer feedback and faster approval, keep PRs between 200 and 400 lines. This makes reviews more manageable and allows for quicker iteration. Using GitHub Insights, Bitbucket, or GitLab allows you to track PR sizes and adjust workflows to encourage smaller, more focused changes that improve collaboration.

6. Code Churn Rate

If you or your team frequently rewrite or modify the same code soon after committing it, your project likely has a high code churn rate. This usually means the initial implementation was unclear, rushed, or overly complex.

A file that’s modified more than three or four times quickly after committing is a red flag. High churn increases maintenance costs, creates instability, and signals poor planning.

CodeScene allows you to track code churn, identify unstable areas, and adjust your development approach to create more stable and maintainable code.

7. Change Failure Rate (CFR)

Change Failure Rate measures how frequently deployments require fixes or rollbacks. If your team often releases updates that break functionality, it’s a sign that your complex code sections are difficult to modify safely.

DORA benchmarks classify teams as follows:

• Elite Teams: <15% CFR (minimal failures, stable releases)
• Low Performers: 45 %+ CFR (frequent failures, high rework)

A high CFR suggests that your system is fragile, and small changes have unexpected consequences. Axify's DORA metrics dashboard helps you monitor CFR and improve deployment reliability by refining your development and testing processes.

8. Time to Review & Merge

The time it takes to review and merge a PR directly reflects your team's efficiency and code complexity. When a PR remains open for too long, it typically means the changes are too large, unclear, or introduce risky modifications.

An ideal review time for most teams is under 24-48 hours. Developers might struggle with a challenging code structure or unclear logic if it takes significantly longer. Using Axify, GitHub, GitLab, and Bitbucket allows you to track review times and adjust workflows to speed up approvals while maintaining high-quality code.

How to Fix Cognitive Complexity

Cognitive complexity slows you down and makes your code harder to maintain. To write cleaner, more understandable code, you must simplify the logic, improve the structure, and leverage automation. Here’s how to reduce complexity and make your code easier to work with.

1. Use Refactoring Techniques

Refactoring helps you simplify code by breaking large functions into smaller, focused methods. 

For example, an analysis of 287,813 refactoring operations across 150 systems found that developers refactor primarily for code simplification (57.8%), performance improvement (25.4%), and bug fixing (16.8%). This highlights the importance of restructuring code to maintain readability.

The point is to make logic easier to follow and reduce mental effort when debugging. Instead of using deeply nested conditionals, you can replace them with polymorphism for better structure.

Using meaningful names for functions and variables also makes your code more intuitive for you and your team. For example, a well-named function like calculateTotalPrice() is far clearer than a vague calc() or doSomething().

2. Reduce Cyclomatic Complexity

High cyclomatic complexity means your code has too many branching paths, which makes it harder to test and understand. You can reduce complexity by using guard clauses to exit functions early instead of relying on deeply nested conditionals.

For example, instead of writing:

You can simplify it with a guard clause:

Using switch cases or dictionaries instead of long if-else chains also makes your logic clearer and easier to follow.

3. Apply Design Patterns

When your code relies on too many conditionals, the strategy pattern can help. Instead of checking conditions to determine behavior, you define interchangeable strategies that execute based on the context.

For instance, instead of this:

You can refactor it using strategy objects, making your code more scalable and easier to maintain. Similarly, the Factory Pattern helps you simplify object creation by centralizing logic and reducing redundant code across your project.

4. Improve Code Readability

Readable code is easier to debug and modify. Consistent formatting, linting tools, and meaningful names reduce confusion and improve clarity. A study analyzing 25 code features found that meaningful names significantly improve readability. Features like naming consistency and structured formatting positively impact how quickly developers understand a codebase. 

Writing tests also enhances readability because well-defined test cases clarify the expected behavior of different functions.

Instead of writing excessive comments, you should focus on self-documenting code where function names and variables describe their purpose.

5. Leverage Automation to Detect Complexity

Automation helps you identify cognitive complexity before it becomes a problem. SonarQube can help you analyze your code and highlight areas that need refactoring. You can integrate complexity analysis into your CI pipeline to catch overly complex logic during pull requests.

Setting up automated checks allows you to ensure your team follows best practices and maintains a clean code structure. Using a comprehensive approach, such as combining automation with regular code reviews, helps you prevent complexity from creeping into your project over time.

Wrapping Up

Cognitive complexity makes your code harder to read, test, and maintain. Here, you’ve learned how factors like deep nesting levels, poor naming, and large functions increase complexity and how techniques like refactoring, design patterns, and automation help you simplify your code.

Keeping things structured and readable allows you to reduce frustration and make development smoother for yourself and your team.

Axify helps you identify potential bottlenecks that could signal increasing complexity, allowing teams to take action before they slow development. Metrics like work item age, PR review time, and DORA metrics provide insights into delivery performance, while the Value Stream Mapping (VSM) tool helps you visualize and optimize your processes.

Ready to make your development process more efficient? Book a demo with Axify and see how it can help your team today.