What is code complexity?

Code complexity is almost unavoidable in modern software development. High code complexity, when not tackled on time, leads to an increase in bugs, and technical debt, and negatively impacts the performance. 

Let’s dive in further to explore the concept of cognitive complexity in software. 

What is code complexity?

Code complexity refers to how difficult it is to understand, modify, and maintain the software codebase. It is influenced by various factors such as lines of code, code structure, number of dependencies, and algorithmic complexity. 

Code complexity exists at multiple levels including the system architecture level, within individual modules or single code blocks. 

The more the code complexity, the more complex a piece of code is. Hence, developers use it to make efforts to minimize it wherever possible. By managing code complexity, developers can reduce costs, improve software quality, and provide a better user experience. 

Why does measuring code complexity matter?

Reduce bugs

In complex code, it becomes difficult to identify the root cause of bugs. Hence, making debugging a more arduous job. These changes can further have unintended consequences due to unforeseen interactions with other parts of the system. By measuring code complexity, developers can particularly complex identity areas that they can further simplify to reduce the number of bugs and improve the overall reliability of the software. 

Increase collaboration

Managing code complexity increases collaboration between team members. Identifying areas of code that are particularly complex requires additional expertise. Hence, enhancing the shared understanding of code by reviewing, refactoring, or redesigning these areas to improve code maintainability and readability.

Better testing

High code complexity presents various challenges for testing such as increased test case complexity and reduced test coverage. Code complexity metrics help testers assess the adequacy of test coverage. It allows them to indicate areas of the code that may require thorough testing and validation. Hence, they can focus on high code complexity areas first and then move on to lower complexity areas.

Enhance performance

Complex code can also impact performance as complex algorithms and data structures can lead to slower execution times and excessive memory consumption. It can further hinder software performance in the long run. Managing code complexity encourages adherence to best practices for writing clean and efficient code. Hence, enhancing the performance of their software systems and delivering better-performing applications to end-users.

How does code complexity increase?

Poor readability and clarity

High code readability leads to an increase in code quality. However, when the code is complex, it lacks readability. This further increases the cognitive load of the developers and slows down the software development process. 

The overly complex code is less modular and reusable which hinders the code clarity and maintenance. 

Lack of code documentation

The main purpose of documentation is to help engineers work together to build a product and have clear requirements of what needs to be done. The unavailability of documentation may make developers’ work difficult since they have to revisit tasks, undefined tasks, and code overlapping and duplications. 

Problematic architectural decisions

Architectural decisions dictate the way the software is written, how to improve it, tested against, and much more. When such decisions are not well documented or communicated effectively, it may lead to misunderstandings and inconsistency in implementation. Moreover, when the architectural decisions are not scalable, it may make the codebases difficult to extend and maintain as the system grows. 

High coupling

Coupling refers to the connection between one piece of code to another. However, it is to be noted that they shouldn’t be highly dependent on each other. Otherwise, it leads to high coupling. It increases the interdependence between modules which makes the system more complex and difficult to understand. Moreover, it also makes the code difficult to isolate and test them independently. 

Metrics to measure code complexity

Cyclomatic complexity

Cyclomatic complexity was developed by Thomas J. Mccabe in 1976. It is a crucial metric that determines the given piece of code complexity. It measures the number of linearly independent paths through a program’s source code. It is suggested cyclomatic complexity must be less than 10 for most cases. More than 10 means the need for refactoring the code. 

To effectively implement this formula in software testing, it is crucial to initially represent the source code as a control flow graph (CFG). The CFG is a directed graph comprising nodes, each representing a basic block or a sequence of non-branching statements, and edges denoting the control flow between these blocks. 

Once the CFG for the source code is established, cyclomatic complexity can be calculated using one of the three approaches: 

  • Basic cyclomatic complexity formula: Cyclomatic Complexity = E – N + 2P, where E corresponds to edges, N to nodes, and P to connected components.
  • Counting decision points: Cyclomatic Complexity = Number of decision points + 1.
  • Summing up predicate nodes: Cyclomatic Complexity = Sum of all predicate nodes + 1.

In each approach, an integer value is computed, indicating the number of unique pathways through the code. This value not only signifies the difficulty for developers to understand but also affects testers’ ability to ensure optimal performance of the application or system. 

Higher values suggest greater complexity and reduced comprehensibility, while lower numbers imply a more straightforward, easy-to-follow structure.

The primary components of a program’s CFG are:

  • Nodes: Individual commands or statements.
  • Edges: Connections between nodes.
  • Connected components: Segments of the graph that are isolated from one another.

For instance, let’s consider the following simple function:

def simple_function(x):

    if x > 0:

        print(“X is positive”)


        print(“X is not positive”)

In this scenario:

E = 2 (number of edges)

N = 3 (number of nodes)

P = 1 (single connected component)

Using the formula, the cyclomatic complexity is calculated as follows: CC = 2 – 3 + 2*1 = 1

Therefore, the cyclomatic complexity of this function is 1, indicating very low complexity.

This metric comes in many built-in code editors including VS code, linters (FlakeS and jslinter), and IDEs (Intellij). 

Cognitive complexity

Sonar developed a cognitive complexity metric that evaluates the understandability and readability of the source code. It considers the cognitive effort required by humans to understand it. It is measured by assigning weights to various program constructs and their nesting levels. 

The cognitive complexity metric helps in identifying code sections and complex parts such as nested loops or if statements that might be challenging for developers to understand. It may further lead to potential maintenance issues in the future. 

Low cognitive complexity means it is easier to read and change the code, leading to better-quality software. 

Halstead volume

Halstead volume metric was developed by Maurice Howard Halstead in 1977. It analyzes the code’s structure and vocabulary to gauge its complexities. 

The formula of Halstead volume: 

N * log 2(n)

Where, N = Program length = N1 + N2 (Total number of operators + Total number of operands) 

n = Program vocabulary = n1 + n2 (Number of operators + number of operands) 

The Halstead volume considers the number of operators and operands and focuses on the size of the implementation of the module or algorithm. 

Rework ratio

The rework ratio measures the amount of rework or corrective work done on a project to the total effort expended. It offers insights into the quality and efficiency of the development process. 

The formula of the Rework ratio: 

Total effort / Effort on rework * 100 

Where, Total effort = Cumulative effort invested in the entire project

Effort on rework = Time and resources spent on fixing defects, addressing issues, or making changes after the initial dev phase 

While rework is a normal process. However, a high rate of rework is considered to be a problem. It indicates that the code is complex, prone to errors, and potential for defects in the codebase.

Maintainability index

This metric measures the score of how easy it is to maintain code. The maintainability index is a combination of 4 metrics – Cyclomatic complexity, Halstead volume, LOC, and depth of inheritance. Hence, giving an overall picture of complexity. 

The formula of the maintainability index: 

171 – 5.2 * ln(V) – 0.23 * (G) – 16.2 * ln(LOC)

The higher the score, the higher the level of maintainability. 

0-9 = Very low level of maintainability 

10-19 = Low level of maintainability 

20-29 = Moderate level of maintainability 

30-100 = Good level of maintainability 

This metric determines the potential challenges and costs associated with maintaining and evolving a given software system. 

Lines of executable code or lines of source code

It is the easiest way to calculate and purely look at the number of LOCs. LOC includes instructions, statements, and expressions however, typically excludes comments and blank lines. 

Counting lines of executable code is a basic measure of program size and can be used to estimate developers’ effort and maintenance requirements. However, it is to be noted that it alone doesn’t provide a complete picture of code quality or complexity. 

How to reduce code complexity?

Create clear requirements

The requirements should be clearly defined and well-documented. A clear roadmap should be established to keep projects on track and prevent feature creep and unnecessary complexities. 

It helps in building a solid foundation for developers and maintains the project’s focus and clarity. The requirements must ensure that the developers understand what needs to be built reducing the likelihood of misinterpretation. 

Prioritize modular design

Break down software into smaller, self-contained modules. Each module must have a single responsibility i.e. focus on specific functions to make it easier to understand, develop, and maintain the code. 

It is a powerful technique to manage complex code as well as encourages code reusability and readability. 


Refactor continuously to eliminate redundancy, improve code readability and clarity, and adhere to best practices. It also helps streamline complex code by breaking down it into smaller, more manageable components. 

Through refactoring, the development team can identify and remove redundant code such as dead code, duplicate code, or unnecessary branches to reduce the code complexity and enhance overall software quality.

Code reviews

Code reviews help maintain code quality and avoid code complexity. It identifies areas of code that may be difficult to understand or maintain later. Moreover, peer reviews provide valuable feedback and in-depth insights regarding the same. 

There are many code review tools available in the market. They include automated checks for common issues such as syntax errors, code style violations, and potential bugs and enforce coding standards and best practices. This also saves time and effort and makes the code review process smooth and easy. 

How Typo can improve your code review process?

Typo’s automated code review tool not only enables developers to catch issues related to maintainability, readability, and potential bugs but also can detect code smells. It identifies issues in the code and auto-fixes them before you merge to master. This means less time reviewing and more time for important tasks. It keeps the code error-free, making the whole process faster and smoother.

Key features

  • Supports top 8 languages including C++ and C#
  • Understands the context of the code and fixes issues accurately
  • Optimizes code efficiently
  • Standardizes code and reduces the risk of a security breach
  • Provides automated debugging with detailed explanations


Understanding and addressing code complexity is key to ensuring code quality and software reliability. By recognizing its causes and adopting strategies to reduce them, development teams can mitigate code complexity and enhance code maintainability, understandability, and readability.