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.
What is Cyclomatic Complexity?
Cyclomatic Complexity, a software metric, was developed by Thomas J. Mccabe in 1976. It is a metric that indicates the complexity of the program by counting its decision points.
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.
Cyclomatic Complexity is calculated using a control flow graph:
M = E - N + 2P
M = Cyclomatic Complexity
N = Nodes (Block of code)
E = Edges (Flow of control)
P = Number of Connected Components
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 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. It presents us with two potential paths:
Path One: Greet the user without a farewell.
Path Two: Greet the user followed by a farewell if is true.
By adding this decision point, the cyclomatic complexity increases to 2. This means there are two unique ways the function might execute, depending on the value of the parameter.
Key Takeaway: Cyclomatic complexity helps in understanding how many independent 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.
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.
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:
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.
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.
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.
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.
Modularity: This means dividing large, complex functions into smaller, modular units so that each piece serves a focused purpose. 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.
Conditional Logic Simplification
Guard Clauses: Developers must implement guard clauses to exit from a function as soon as a condition is met. This avoids deep nesting and enhances 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.
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.
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
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.
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.
Typo - An Automated Code Review Tool
Typo’s automated code review tool identifies issues in your code and auto-fixes them before you merge to master. 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 top 8 languages including C++ and C#.
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
The cyclomatic complexity metric is critical in software engineering. Reducing cyclomatic complexity increases the code maintainability, readability, and simplicity. 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.
