Are you tired of dealing with buggy, hard-to-understand code? Measuring cyclomatic complexity can help you identify potential issues before they become major problems. Keep reading to find out how to do that.
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High cyclomatic complexity can be a sign of a technical debt that can slow down your development process and lead to bugs and outages. In this article, we'll show you how to measure, analyze, and reduce cyclomatic complexity effectively.
Cyclomatic complexity is a software quality metric that measures the complexity of a program. It helps us understand how hard the code is to test and maintain.
To calculate it, we count the number of independent paths through the code. Every decision point in the code increases the cyclomatic complexity by one. High cyclomatic complexity may mean that the code has complex logic or isn't modular enough.
By keeping cyclomatic complexity in check, you can improve the maintainability and reliability of your software. However, it can also pose some risks, like ignoring user experience, over-engineering, and having a false sense of security - more on that later.
<span class="colorbox1" fs-test-element="box1"><p>Note: Sole measuring, without taking action, won’t give you any of those benefits.</p></span>
Measuring cyclomatic complexity helps you to identify overly complex logic or insufficient modularity in your code. By addressing these issues, you can improve the overall quality of our code.
Lower cyclomatic complexity makes it easier to read and understand the code, which in turn makes it easier to maintain and update. By measuring cyclomatic complexity regularly, you can ensure that your code remains easy to maintain and update.
High cyclomatic complexity can lead to more defects in the code. By measuring it regularly, you can identify potential issues early and prevent them from becoming defects.
Measuring cyclomatic complexity helps you identify the number of independent paths through the code. This, in turn, helps us write more effective tests that cover all possible paths through the code.
By measuring cyclomatic complexity, we can identify areas of our code that need improvement. This can help us focus our efforts and increase our productivity.
Code with high cyclomatic complexity is more complex and harder to understand, which can make it more difficult to identify and fix defects. Developers may not fully understand all the possible paths through the code, or they may miss certain conditions or edge cases that could lead to defects.
Code with high cyclomatic complexity can also be more difficult to test, as there are more paths that need to be tested and more conditions that need to be covered. This can result in gaps in test coverage, where certain conditions or paths are not tested, which can increase the risk of defects.
High cyclomatic complexity can be an indicator of other issues, such as excessive nesting or overly complex conditional logic, which can also contribute to a higher risk of defects. Code with these issues can be more error-prone and harder to understand, which can lead to defects and bugs.
<span class="colorbox1" fs-test-element="box1"><p>Read also: Prioritizing technical debt can be hard. See how a development team coped with tech debt by identifying areas for improvement that support business, not just clean the code.</p></span>
There are tools that can calculate cyclomatic complexity for you, e.g. SonarQube or IDE plugins for code metrics.
Want to calculate it by yourself either way?
To calculate cyclomatic complexity, you need to identify the different paths that can be taken through a block of code.
You can do this by constructing a Control Flow Graph, which represents the different paths that can be taken through the code. Then, you count the number of regions in the graph to determine the cyclomatic complexity.
A region is defined as an area bounded by decision points, where a decision point is a statement that can lead to multiple paths. For example, an "if" statement or a "switch" statement can be a decision point.
Let's look at an example. Consider the following code:
if (a > b) {
doSomething();
} else {
doSomethingElse();
}
We can construct a Control Flow Graph as follows:
+-----+
| a>b |
+-----+
|
v
+-----------+
| doSomething|
+-----------+
|
v
+---------------+
| doSomethingElse|
+---------------+
The graph has two regions, one for each branch of the "if" statement. Therefore, the cyclomatic complexity of this code is 2.
Long methods with multiple nested loops and conditional statements are more difficult to read and understand. Break up long methods into smaller, more focused methods that are easier to comprehend and maintain.
Refactor the code to simplify it and reduce the number of decision points. For example, you can extract complex logic into separate methods or functions, which can reduce the number of decision points and make the code easier to read and understand.
<span class="colorbox1" fs-test-element="box1"><p>Read also: 10 most common technical debt examples, each one with explanation, spotting tips, and the guidance on how to avoid & how to solve this problem if it already exists.</p></span>
Instead of nesting loops, use conditional statements to control the flow of the program. For example, you can use an if statement to break out of a loop when a condition is met instead of using a nested loop.
If a method or function has too many parameters, it can increase the cyclomatic complexity. Reduce the number of parameters by grouping related variables into objects or structs.
Dead code, such as unused variables or unreachable statements, can increase the cyclomatic complexity. You can eliminate dead code to simplify the it and reduce the cyclomatic complexity.
Write simpler code by avoiding unnecessary complexity and keeping it as simple as possible. For example, use descriptive variable names and avoid complex expressions.
Focusing solely on cyclomatic complexity can lead to neglecting other important factors related to code quality, such as consistency, clarity, and readability. As a result, the code may become difficult to maintain and update, even if its cyclomatic complexity is low.
A high cyclomatic complexity may not necessarily mean that there is a problem with the code. Focusing solely on cyclomatic complexity, you may end up over-engineering the code to reduce the complexity, which can result in wasted time and effort.
Cyclomatic complexity doesn't measure the user experience of the software. If you focus solely on reducing cyclomatic complexity, you may overlook usability issues that can negatively impact the user experience.
A low cyclomatic complexity doesn't necessarily mean that the code is easy to maintain and update. If you base your decisions solely on cyclomatic complexity results, you may have a false sense of security and overlook potential issues that can arise during maintenance and updates.
Cyclomatic complexity may not be applicable to certain types of code, such as machine learning algorithms or embedded systems. Beware of overlooking other metrics and factors that are more relevant to these types of systems.
Measuring cyclomatic complexity requires knowledge of software engineering and metrics. If not properly understood, the results can be misinterpreted, leading to incorrect conclusions.
High cyclomatic complexity can indicate potential issues with code quality, but it doesn't necessarily mean that there is a problem. It's important to consider other factors, such as the context in which the code is used, before taking any action.
There are several alternatives to cyclomatic complexity that can be used to evaluate code complexity and identify potential issues:
This metric is based on the number of unique operators and operands in a piece of code and can be used to estimate the difficulty of understanding and maintaining the code. Halstead Metrics can provide a more comprehensive view of code complexity than cyclomatic complexity, as it takes into account both the control flow and the vocabulary of the code.
Maintainability Index takes into account various factors such as cyclomatic complexity, code duplication, and code size, to provide an overall measure of code maintainability. It can be a useful alternative to cyclomatic complexity when looking for a more holistic measure of code complexity and maintainability.
This metric simply measures the number of lines of code in a piece of software. While not as sophisticated as other metrics, it can be a useful indicator of code complexity and maintainability. In general, the more lines of code there are, the more complex and difficult to maintain the code is likely to be.
In general, a combination of metrics is likely to provide the most accurate and comprehensive view of code complexity and maintainability.
By understanding the relationship between cyclomatic complexity and software quality, teams can make more informed decisions about their codebase and improve the maintainability and scalability of their applications.
The key to success is to choose the right set of metrics tailored to your product goals. Find your perfect set by exploring other software quality metrics in this handbook.
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Authors
Software Quality Assurance Engineer with 15 years of professional experience. Responsible for the whole QA line at Brainhub.
Software development enthusiast with 6 years of professional experience in the tech industry.
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