How to Reduce Cyclomatic Complexity?

Think of reading a book with multiple plot twists and branching storylines. While engaging, it can also be confusing and overwhelming when there are too many paths to follow. Just as a complex storyline can confuse readers, high Cyclic Complexity can make code hard to understand, maintain, and test, leading to bugs and errors.

In this blog, we will discuss why high cyclomatic complexity can be problematic and ways to reduce it.

Cyclomatic Complexity, a software metric, was developed by Thomas J. Mccabe in 1976. It is a metric that indicates the complexity of a program by counting its decision points and helps measure the overall complexity of a program. Every decision point in the code increases the cyclomatic complexity by one, making it a useful tool for understanding the intricacy of control flow.

A higher cyclomatic Complexity score reflects more execution paths, leading to increased complexity. On the other hand, a low score signifies fewer paths and, hence, less complexity. A cyclomatic complexity score greater than 10 typically suggests that the code may be too complex and could benefit from refactoring.

Cyclomatic Complexity is calculated using a control flow graph constructed from the program's source code:

M = E - N + 2P

M = Cyclomatic Complexity

N = Nodes (Block of code)

E = Edges (Flow of control)

P = Number of Connected Components

To calculate cyclomatic complexity, you can manually count the nodes, edges, and connected components in the control flow graph and apply the formula above, or use automated tools that analyze the program's source code to determine the complexity score.

Introduction to Code Complexity

Code complexity defines the challenge level software teams encounter when analyzing, testing, and maintaining application components. Within software development workflows, elevated code complexity streamlines can significantly hinder project velocity, amplify defect introduction rates, and escalate long-term maintenance expenditures. Leveraging widely-adopted software quality metrics for evaluating code complexity, cyclomatic complexity stands out as a fundamental assessment tool. This metric analyzes the total count of linearly independent execution paths traversing a program's source code, delivering quantitative insights into control flow intricacy levels. While cyclomatic complexity is a key metric, other measures like Lines of Code (LoC) provide a basic measure of size but do not fully capture the intricacies of code complexity. Calculating cyclomatic complexity involves constructing control flow graphs that visually map diverse pathways and decision nodes embedded within the codebase architecture. By continuously monitoring code complexity patterns and utilizing metrics like cyclomatic complexity, development teams can proactively optimize technical debt management, enhance code quality standards, and ensure their software applications remain robust and maintainable—even as system scale and functionality requirements evolve.

Understanding Cyclomatic Complexity Through a Simple Example

Let’s delve into the concept of cyclomatic complexity with an easy-to-grasp illustration.

Imagine a function structured as follows:

function greetUser(name) {    
print(`Hello, ${name}!`);
}

In this case, the function is straightforward, containing a single line of code. Since there are no conditional code paths, the cyclomatic complexity is 1—indicating a single, linear path of execution.

Now, let’s add a twist:

function greetUser(name, offerFarewell = false) {    
	print(`Hello, ${name}!`);        
		if (offerFarewell) {        
	print(`Goodbye, ${name}!`);    
	}
}

‍

In this modified version, we’ve introduced a conditional statement. The use of the if statement introduces additional code paths, as the function can now execute in more than one way. It presents us with multiple paths through the code:

1. Code Path One: Greet the user without a farewell.
2. Code Path Two: Greet the user followed by a farewell if offerFarewell is true.

By adding this decision point, the cyclomatic complexity increases to 2. This means there are multiple code paths the function might execute, depending on the value of the offerFarewell parameter. Even simple functions can develop complex logic as more conditions are added.

Key Takeaway: Cyclomatic complexity helps in understanding how many independent code paths there are through a function, aiding in assessing the possible scenarios a program can take during its execution. This is crucial for debugging and testing, ensuring each path is covered.

Calculating Complexity

Leveraging cyclomatic complexity calculations transforms how development teams understand and optimize their codebase intricacy. The proven formula M = E - N + 2P streamlines complexity measurement, where M represents the cyclomatic complexity metric, E captures the edges within the control flow graph, N defines the total nodes, and P identifies connected components throughout the system. This powerful calculation revolutionizes code analysis by revealing independent pathways and decision points that exist within programs, pinpointing areas where complexity may hinder development velocity. Elevated cyclomatic complexity often signals the presence of intricate code segments that challenge testing efficiency, debugging workflows, and long-term maintainability. By consistently measuring and monitoring cyclomatic complexity, development teams can strategically identify which codebase segments would benefit from targeted refactoring initiatives, ultimately streamlining complexity and enhancing overall code maintainability. Harnessing automated tools to calculate and continuously monitor complexity ensures that software architectures remain robust, reliable, and positioned to evolve efficiently over time.

Why is High Cyclomatic Complexity Problematic? 

Increases Error Prone 

The more complex the code is, the more the chances of bugs. When there are many possible paths and conditions, developers may overlook certain conditions or edge cases during testing. This leads to defects in the software and becomes challenging to test all of them. 

Impact of Cyclomatic Complexity on Testing

Cyclomatic complexity plays a crucial role in determining how we approach testing. By calculating the cyclomatic complexity of a function, developers can ascertain the minimum number of test cases required to achieve full branch coverage. This metric is invaluable, as it predicts the difficulty of testing a particular piece of code.

Higher values of cyclomatic complexity necessitate a greater number of test cases to comprehensively cover a block of code, such as a function. This means that as complexity increases, so does the effort needed to ensure the code is thoroughly tested. For developers looking to streamline their testing process, reducing cyclomatic complexity can greatly ease this burden, making the code not only less error-prone but also more efficient to work with.

Leads to Cognitive Complexity 

Cognitive complexity refers to the level of difficulty in understanding a piece of code. High maintainability index scores typically indicate that the code is easier to maintain, while low scores suggest areas that may require refactoring.

Cyclomatic Complexity is one of the factors that increases cognitive complexity. Since, it becomes overwhelming to process information effectively for developers, which makes it harder to understand the overall logic of code.

Difficulty in Onboarding 

Codebases with high cyclomatic Complexity make onboarding difficult for new developers or team members. The learning curve becomes steeper for them and they require more time and effort to understand and become productive. This also leads to misunderstanding and they may misinterpret the logic or overlook critical paths. 

Higher Risks of Defects

More complex code leads to more misunderstandings, which further results in higher defects in the codebase. Complex code is more prone to errors as it hinders adherence to coding standards and best practices. 

Rise in Maintainance Efforts 

Due to the complex codebase, the software development team may struggle to grasp the full impact of their changes which results in new errors. This further slows down the process. It also results in ripple effects i.e. difficulty in isolating changes as one modification can impact multiple areas of application. 

To truly understand the health of a codebase, relying solely on cyclomatic complexity is insufficient. While cyclomatic complexity provides valuable insights into the intricacy and potential risk areas of your code, it's just one piece of a much larger puzzle.

Here's why multiple metrics matter:

1. Comprehensive Insight: Cyclomatic complexity measures code complexity but overlooks other aspects like code quality, readability, or test coverage. Incorporating metrics like code churn, test coverage, and technical debt can reveal hidden challenges and opportunities for improvement. Halstead Metrics measures are a set of software metrics that provide a quantitative assessment of the complexity and maintainability of a program based on its operators and operands.
2. Balanced Perspective: Different metrics highlight different issues. For example, maintainability index offers a perspective on code readability and structure, whereas defect density focuses on the frequency of coding errors. By using a variety of metrics, teams can balance complexity with quality and performance considerations.
3. Improved Decision Making: When decisions hinge on a single metric, they may lead to misguided strategies. For instance, reducing cyclomatic complexity might inadvertently lower functionality or increase lines of code. A balanced suite of metrics ensures decisions support overall codebase health and project goals.
4. Holistic Evaluation: A codebase is impacted by numerous factors including performance, security, and maintainability. By assessing diverse metrics, teams gain a holistic view that can better guide optimization and resource allocation efforts.

In short, utilizing a diverse range of metrics provides a more accurate and actionable picture of codebase health, supporting sustainable development and more effective project management.

How to Reduce Cyclomatic Complexity? 

Function Decomposition

• Single Responsibility Principle (SRP): This principle states that each module or function should have a defined responsibility and one reason to change. If a function is responsible for multiple tasks, it can result in bloated and hard-to-maintain code. Functions with too many parameters often violate this principle, as they tend to handle more than one responsibility.
• Modularity: This means dividing large, complex functions into smaller, modular units so that each piece serves a focused purpose. Large functions with too many parameters can increase complexity and should be broken down into smaller, focused functions to improve clarity and maintainability. It makes individual functions easier to understand, test, and modify without affecting other parts of the code.
• Cohesion: Cohesion focuses on keeping related code close to functions and modules. When related functions are grouped together, it results in high cohesion which helps with readability and maintainability.
• Coupling: This principle states to avoid excessive dependencies between modules. This will reduce the complexity and make each module more self-contained, enabling changes without affecting other parts of the system.

The primary goal in minimizing cyclomatic complexity is to simplify control flow and reduce the number of decision points in a function.

Conditional Logic Simplification

Simplifying control structures such as if statements and loops is key to reducing code complexity and improving maintainability. Effective strategies to reduce cyclomatic complexity include refactoring large functions into smaller, single-purpose functions and simplifying conditional statements.

• Guard Clauses: Developers must implement guard clauses to exit from a function as soon as a condition is met. This avoids deep nesting and helps prevent nested control structures, enhancing the readability and simplicity of the main logic of the function.
• Boolean Expressions: Use De Morgan’s laws and simplify Boolean expressions to reduce the complexity of conditions. For example, rewriting! (A && B) as ! A || !B can sometimes make the code easier to understand. Additionally, simplifying nested structures and refactoring nested loops can further reduce complexity and improve code clarity.
• Conditional Expressions: Consider using ternary operators or switch statements where appropriate. This will condense complex conditional branches into more concise expressions which further enhance their readability and reduce code size. Replacing deeply nested if statements with simpler control structures can also significantly improve code readability.
• Flag Variables: Avoid unnecessary flag variables that track control flow. Developers should restructure the logic to eliminate these flags which can lead to simpler and cleaner code.

Loop Optimization

• Loop Unrolling: Expand the loop body to perform multiple operations in each iteration. This is useful for loops with a small number of iterations as it reduces loop overhead and improves performance.

• Loop Fusion: When two loops iterate over the same data, you may be able to combine them into a single loop. This enhances performance by reducing the number of loop iterations and boosting data locality.

• Loop Strength Reduction: Consider replacing costly operations in loops with less expensive ones, such as using addition instead of multiplication where possible. This will reduce the computational cost within the loop.

• Loop Invariant Code Motion: Prevent redundant computation by moving calculations that do not change with each loop iteration outside of the loop. 

Code Refactoring

• Extract Method: Move repetitive or complex code segments into separate functions. This simplifies the original function, reduces complexity, and makes code easier to reuse.

• Introduce Explanatory Variables: Use intermediate variables to hold the results of complex expressions. This can make code more readable and allow others to understand its purpose without deciphering complex operations.

• Replace Magic Numbers with Named Constants: Magic numbers are hard-coded numbers in code. Instead of directly using them, create symbolic constants for hard-coded values. It makes it easy to change the value at a later stage and improves the readability and maintainability of the code.

• Simplify Complex Expressions: Break down long, complex expressions into smaller, more digestible parts to improve readability and reduce cognitive load on the reader.

5. Design Patterns

Object oriented programming enables the use of design patterns to reduce cyclomatic complexity by replacing complex decision structures with more maintainable code.

• Strategy Pattern: This pattern allows developers to encapsulate algorithms within separate classes. By delegating responsibilities to these classes, you can avoid complex conditional statements and reduce overall code complexity.
• State Pattern: When an object has multiple states, the State Pattern can represent each state as a separate class. This simplifies conditional code related to state transitions.
• Observer Pattern: The Observer Pattern helps decouple components by allowing objects to communicate without direct dependencies. This reduces complexity by minimizing the interconnectedness of code components.

6. Code Analysis Tools

• Static Code Analyzers: Static Code Analysis Tools like Typo or Sonarqube, can automatically highlight areas of high complexity, unused code, or potential errors. This allows developers to identify and address complex code areas proactively.

• Code Coverage Tools: Code coverage is a measure that indicates the percentage of a codebase that is tested by automated tests. Tools like Typo measures code coverage, highlighting untested areas. It helps ensure that the tests cover a significant portion of the code which helps identifies untested parts and potential bugs

Removing Duplicate Code

Eliminating redundant code represents a transformative approach to reducing software complexity and enhancing codebase maintainability. Duplicate code blocks—segments that execute identical or nearly identical functions—can rapidly escalate the total lines of code, making the software architecture more challenging to comprehend, navigate, and modify. This redundancy not only amplifies cognitive overhead for development teams but also elevates the probability of inconsistencies and system vulnerabilities when modifications are implemented. To address this challenge, development teams should extract repetitive logic into reusable functions or methods, implement proven architectural patterns, and maintain adherence to established coding standards. AI-powered tools and machine learning algorithms can identify duplicate code patterns, streamlining the development workflow and ensuring that the codebase remains optimized and efficient. By minimizing code redundancy, development teams can significantly reduce complexity, enhance software quality, and make future feature implementations substantially more manageable and scalable.

Eliminating Dead Code

Eliminating dead code constitutes a fundamental practice for controlling software complexity and sustaining a robust, maintainable codebase that facilitates long-term project success. Dead code comprises any executable segments that remain unreachable during runtime or whose computational results never contribute to application functionality, thereby unnecessarily expanding project scope and architectural complexity. This unutilized code obscures primary business logic pathways, creating cognitive overhead that impedes developer comprehension and hampers efficient maintenance workflows throughout the software lifecycle. Systematic code reviews, sophisticated static analysis methodologies, and comprehensive testing strategies serve as effective mechanisms for identifying and purging these redundant code segments from production systems. Through meticulous cleanup of these unused sections, development teams can substantially reduce unnecessary architectural complexity, minimize technical debt accumulation, and enhance code modularity and reusability across project components. Ultimately, the strategic elimination of dead code not only streamlines the overall codebase architecture but also amplifies software quality metrics, maintainability indices, and long-term project sustainability while enabling more efficient resource allocation and accelerated development cycles.

Conducting Code Reviews

Implementing code reviews transforms software development practices and serves as a strategic approach for systematically reducing code complexity across development workflows. Through collaborative examination processes, development teams analyze each other's implementations to ensure adherence to established coding standards, eliminate unnecessary complexity layers, and maintain elevated code quality benchmarks. This collaborative methodology enables teams to identify intricate code segments, redundant implementations, and obsolete code structures before they become deeply integrated within the codebase architecture. Regular review cycles foster comprehensive knowledge transfer, enhance individual coding capabilities, and establish consistency patterns throughout development teams. Automated review platforms further augment these processes by detecting issues such as elevated cyclomatic complexity metrics and recommending optimization strategies. By integrating code reviews as fundamental components of development workflows, teams proactively minimize complexity overhead, strengthen maintainability frameworks, and deliver increasingly reliable software solutions.

Other Ways to Reduce Cyclomatic Complexity 

• Identify andremove dead code to simplify the codebase and reduce maintenance efforts. This keeps the code clean, improves performance, and reduces potential confusion.
• Consolidate duplicate code into reusable functions to reduce redundancy and improve consistency. This makes it easier to update logic in one place and avoid potential bugs from inconsistent changes.
• Continuously improve code structure by refactoring regularly to enhance readability, and maintainability, and reduce technical debt. This ensures that the codebase evolves to stay efficient and adaptable to future needs.
• Perform peer reviews to catch issues early, promote coding best practices, and maintain high code quality. Code reviews encourage knowledge sharing and help align the team on coding standards.
• Write Comprehensive Unit Tests to ensure code functions correctly and supports easier refactoring in the future. They provide a safety net which makes it easier to identify issues when changes are made.

To further limit duplicated code and reduce cyclomatic complexity, consider these additional strategies:

• Extract Common Code: Identify and extract common bits of code into their own dedicated methods or functions. This step streamlines your codebase and enhances maintainability.
• Leverage Design Patterns: Utilize design patterns—such as the template pattern—that encourage code reuse and provide a structured approach to solving recurring design problems. This not only reduces duplication but also improves code readability.
• Create Utility Packages: Extract generic utility functions into reusable packages, such as npm modules or NuGet packages. This practice allows code to be reused across the entire organization, promoting a consistent development standard and simplifying updates across multiple projects.

By implementing these strategies, you can effectively manage code complexity and maintain a cleaner, more efficient codebase. However, keep in mind that low cyclomatic complexity alone does not always guarantee maintainable code—other factors such as code readability, documentation, and modularity should also be considered.

Typo - Unique AI Code Review + SAST Engine

Typo's code review tool identifies issues in your code and auto-fixes them before you merge to master using AI suggestions as well as static code analysis. This means less time reviewing and more time for important tasks. It keeps your code error-free, making the whole process faster and smoother.

Key Features:

• Supports alltop languages

• Understands the context of the code and fixes issues accurately.

• Optimizes code efficiently.

• Provides automated debugging with detailed explanations.

• Standardizes code and reduces the risk of a security breach using OWASP Top 10 framework

‍

Conclusion 

The cyclomatic complexity metric is critical in software engineering. Reducing cyclomatic complexity increases the code maintainability, readability, and simplicity, and can positively impact cycle time in software development. By implementing the above-mentioned strategies, software engineering teams can reduce complexity and create a more streamlined codebase. Tools like Typo's automated code review also help in identifying complexity issues early and providing quick fixes. Hence, enhancing overall code quality.

‍