Understanding cognitive complexity in software development

Taylor Bruneaux


In software development, effectively managing complexity is critical to crafting maintainable and easily understandable code.

When programming code becomes too complicated, it can have a high level of cognitive complexity, which can create challenges, making it harder to maintain and read the code. Understanding the significance of cognitive complexity, the issues it can cause, where it comes from, and how to reduce it is essential. While metrics can measure cognitive complexity, it’s mainly up to humans to interpret and address it.

Developers can use strategies for managing cognitive complexity to make software development easier to understand and maintain. These strategies combine theory with practical steps and balance quantitative measures with developers’ judgment. This approach can improve software quality, making it more accessible and easier to maintain.

This guide will explore cognitive complexity in software engineering, including its definition, sources, understanding, and management.

What is cognitive complexity?

Cognitive complexity is the difficulty level in understanding concepts or solving problems based on the interaction of multiple elements.

In software engineering, cognitive complexity quantifies developers’ challenges in comprehending code or software systems. Unlike traditional metrics focusing only on code structure, cognitive complexity accounts for the human cognitive load in navigating a program’s logic. It evaluates the mental effort required for understanding, debugging, and modifying software, making it a vital measure of software quality.

The cognitive complexity metric assesses the intricacies of control structures, conditional nesting, and program flow, identifying code sections that may need simplification or refactoring to improve code quality and maintainability.

What causes cognitive complexity?

Cognitive complexity in software systems can stem from various factors. For instance, deeply nested conditional statements, excessive use of logical operators, and significant methods or functions with multiple execution paths can significantly contribute to cognitive load.

Complex code patterns, lack of code clarity, and inconsistent coding practices also elevate cognitive complexity.

Additionally, solving a problem’s inherent difficulty coupled with the nuances of the chosen programming language can increase developers’ cognitive burden. Recognizing these causes is the first step toward managing and reducing cognitive complexity.

Understanding and measuring cognitive complexity

In software development, measuring and understanding cognitive complexity involves evaluating the mental effort required to comprehend code, focusing on how the control structures and program flow add to the overall complexity.

Cognitive complexity is calculated by starting with one point and then adding points for each decision point, nesting level, flow-breaking action, recursion, and use of logical operators in conditions. The total number of points shows how challenging the code is to understand.

Understanding this metric requires developers to look at the raw numbers and consider the context in which the code operates, including comments, naming conventions, and the modularization of code into smaller, more manageable pieces.

Why is cognitive complexity dangerous?

High cognitive complexity in software can be difficult for several reasons. It increases the risk of bugs and errors as developers misunderstand complex logic or overlook crucial details. Moreover, it can lead to higher rework ratios and technical debt, as time-consuming revisions become necessary to fix issues stemming from initially misunderstood code.

High cognitive complexity also hampers maintainability, making future modifications, updates, and enhancements more challenging and resource-intensive. In software development, where adaptability and efficiency are paramount, such impediments can significantly detract from software quality and project success.

The difference between cognitive complexity and cyclomatic complexity

Cognitive complexity and cyclomatic complexity are two distinct metrics used to assess different aspects of code, offering insights into its maintainability and understandability. Yet, neither serves as a definitive measure of code quality.

Cognitive complexity focuses on the mental effort required to understand the code, considering factors like flow control, nesting levels, and the readability of code constructs, aiming to quantify how challenging it is for a human to follow the logic. On the other hand, Cyclomatic Complexity measures the number of linearly independent paths through a program’s source code, reflecting its structural complexity based on decision points like loops and branches.

While cyclomatic complexity offers a quantitative look at the potential testing effort needed by counting the paths that might need to be tested, it doesn’t directly address the ease with which code can be understood or modified. Conversely, cognitive complexity provides a more nuanced view of the challenges in parsing and working with the code from a human perspective.

However, both metrics have limitations and should be used as part of a broader assessment strategy, considering that high-quality code is not solely defined by its complexity but also by factors such as its functionality, efficiency, and adaptability to change.

Can cognitive complexity in software be improved?

Addressing cognitive complexity in software is possible and essential for enhancing code quality and developer productivity. The following strategies can help manage cognitive complexity and improve code quality.

Refactoring and simplification

Breaking down extensive, complex methods into smaller, more manageable functions can significantly reduce cognitive load. Simplifying control structures and reducing nesting using guard clauses or early returns also helps.

Consistent coding practices

Adhering to consistent coding standards and practices across the team or project can make the codebase more predictable and easier to understand.

Code reviews

Implementing thorough code reviews as part of the development process allows team members to identify complex code areas and suggest improvements. Peer feedback is invaluable for maintaining low cognitive complexity.

Design patterns

Applying established design patterns can help manage complexity by providing proven solutions to common problems, making the overall structure of the codebase more intuitive.

Continuous learning and adaptation

Encouraging ongoing education and adaptation of new, more straightforward coding techniques or languages can contribute to a culture of simplicity and clarity, countering the rise of cognitive complexity.

Emphasizing qualitative alongside quantitative

While metrics, including the cognitive complexity metric, provide valuable insights into code quality, they should not replace human judgment. Program comprehension is subjective, meaning that what is complex for one developer may be straightforward for another. Software developers should use metrics as guides rather than absolute measures, always considering the specific context and the team’s collective understanding and preferences.

Metrics like cyclomatic and cognitive complexity offer quantitative assessments but cannot fully capture the nuances of human cognition or the intricacies of software engineering. Developers and teams should strive for a balanced approach, leveraging metrics to inform but not dictate their decisions on code quality and maintainability.


Cognitive complexity in software is a nuanced and critical aspect of software engineering that impacts code quality, developer productivity, and the overall success of software projects. By understanding its causes, dangers, and mitigation strategies, developers can take proactive steps to manage cognitive load, ensuring that software systems are functional but also maintainable and understandable. As the field of software development continues to evolve, fostering an environment that values clarity, simplicity, and human insight will be vital to navigating the complexities of creating high-quality software.

February 7, 2024

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