People often think of code complexity as subjective. What seems like a tangled mess to one developer may be a walk in the park for another. However, code complexity is an objective phenomenon that you can measure and address, especially using the right metrics and tools.
Today’s read will cover everything you need to know about code complexity. We’ll break down what it is and how to measure it, debunk common myths, explore causes and pitfalls, and share actionable tips to keep your code clean and maintainable.
What is code complexity, and why does it matter?
The definition of code complexity is simple: it measures how complex a piece of code is to understand, modify, or test. Let’s quickly jump into an example.
Code version 1
def do_stuff(x, y):
return (lambda x, y: x * y)(x, y)
w = 5
h = 10
a = do_stuff(w, h)
print(f"The area is {a}")
Code version 2
def calculate_area_of_rectangle(width, height):
return width * height
area = calculate_area_of_rectangle(5, 10)
print(f"The area is {area}")
Both above snippets achieve the same results: calculate the area of a rectangle. However, only the code version 2 does an excellent job at making the developer’s intention immediately evident. Code version 1 uses vague functions, variable names, and an unnecessary lambda function for multiplication.
Why does this difference in understandability matter, you ask? There are several reasons:
- Complex code makes it more challenging to add new features, which can slow development.
- Project deadlines are delayed as developers take more time to understand, modify, and test complex code.
- Complex code accumulates over time, especially when developers are forced to prioritize speed over readability. As time passes, addressing this technical debt becomes increasingly difficult and expensive.
- Writing comprehensive test cases for complex code with tangled logic paths is often more complicated. This can lead to hidden bugs slipping through the cracks.
What are the leading causes of code complexity?
Let’s explore some of the most common reasons for code complexity in software:
Poor variable names
Cryptic variable names like "x," "y," or “temp” offer no clues about their purpose and frustrate fellow developers. Descriptive names like "customerName" or "orderTotal" make the code self-documenting and easier to grasp.
Lack of comments
Some argue that it’s too complex if your code requires an explanation. In reality, there are occasions where comments are necessary to convey intent. For example, new developers might not understand its logic if you implement a complex algorithm without comments.
With that said, excessive commenting can clutter the code. The key is to find the right balance: add comments that you would appreciate having in the future.
Too many lines of code
Large functions or class files with hundreds of code lines become overwhelming to navigate and process. For example, you may have a ProcessRequest() function that includes logic for request sanitization, authentication, authorization, database operations, and response generation. Such a function will be excessively long and, hence, hard to understand, increasing the time needed to update it.
The recommended alternative would be to break down the ProcessRequest() function into smaller, more specific functions.
Tight coupling
Highly interdependent code modules are difficult to modify or test in isolation. For example, in a tightly coupled system, changing something in module A may cause unexpected issues in module B. Loose coupling with well-defined interfaces promotes modularity and decreases complexity.
Overly optimized code
Although optimization is essential, excessive focus on squeezing every last bit of performance can lead to convoluted and hard-to-understand code as you optimize for the machine executing the code rather than the human reading it. Developers should aim for a balance between efficiency and readability.
No project documentation
Not documenting your code is like building a house without a blueprint. Without proper documentation, your developers are unsure about the project’s architecture, design decisions, and coding standards. This lack of clarity makes it harder to understand and maintain the code, contributing to increased complexity and inconsistencies in the project.
A well-documented codebase is also easier for new developers to get acquainted with.
Bad design
Poor design choices from the outset can lead to a convoluted code base that is difficult to maintain over time. For example, not following the single responsibility principle can result in classes or functions that try to do too much, making the code base more complex.
Following established design principles is essential to create a well-structured, scalable code base.
What are the advantages of measuring code complexity?
Before we cover ways to measure code complexity, let’s look at some benefits that this activity can offer:
More readable code—> Increased developer productivity—> Faster release cycles
When you measure code complexity, it can encourage developers to write cleaner and more understandable code. This translates to faster development and release cycles, as developers spend less time deciphering complex logic and more time writing new features.
However, according to Goodhart’s Law, focusing too much on specific metrics can have unintended consequences. For example, if we measure function length to avoid overly long functions, developers may split functions prematurely to meet this metric. This can inadvertently increase indirection and cognitive load. Therefore, the success of using these metrics depends on how management integrates them into the broader picture of what constitutes success.
More testable code—> Less bugs
By keeping code complexity in check, developers can write code that’s intrinsically easier to test, which results in fewer bugs, a higher code quality, and happier customers.
Improved collaboration—> Happier developers—> Less turnover
When code is clear and concise, it promotes better cooperation among developers. Everyone is on the same page about new features, and new joiners can quickly understand the codebase. This leads to a happier, more productive development team with lower turnover rates.
Enhanced documentation—> Long-term project success
Clear and concise code is self-documenting. Moreover, when developers use metrics to measure complexity, they become more aware of how complex their code is. This awareness often leads them to add detailed comments and explanations to make the code more understandable.
Well-documented code makes knowledge transfer easy, setting your project up for long-term success.
An optimized codebase—> Less maintenance costs
You can build a more streamlined, fault-tolerant, and efficient codebase by prioritizing lower complexity. With fewer bugs and scalability challenges, such a codebase incurs lower maintenance costs eventually.
Metrics for evaluating code complexity
As mentioned above, code complexity isn’t only a subjective feeling; various code metrics provide quantitative insights into the complexity of software. Here are a few:
Halstead complexity measures
Halstead metrics, like program length, vocabulary size (number of unique operators and operands in the code), and program level (calculated by dividing the number of operator occurrences by the number of operand occurrences), are a great way to quantify the complexity of algorithms and programs.
For example, a higher program length or vocabulary size indicates high complexity. A high vocabulary size typically means more unique elements to track, increasing cognitive load and making the code harder to understand and maintain. This can lead to more errors and a steeper learning curve for new developers.
Cyclomatic complexity
The Mccabe cyclomatic complexity metric calculates the number of independent execution paths within a code section. Execution paths are created by conditional statements (if/else, switch/case), loops, and other decision points.
A higher cyclomatic complexity indicates complex code is more prone to errors or unexpected behaviour because it suggests more possible execution paths within the code. With more paths to consider, there’s an increased chance of overlooking edge cases or unintended scenarios during development and testing.
The formula for cyclomatic complexity is:
M=E−N+2P
Where:
- M is the cyclomatic complexity
- E is the number of edges in the program’s control flow graph.
- N is the number of nodes in the program’s control flow graph.
- P is the number of connected components (i.e., regions of code that aren’t interconnected).
Feature delivery speed ratio
You can define this custom metric to track the relationship between code complexity and development speed. To calculate it, you could divide the time required to implement features by the story points assigned to each feature. Story points are measurement units used in agile setups to estimate effort. They’re relative measures that have meaning only for that team.
A significant slowdown in the delivery speed ratio for features with similar complexity can indicate underlying code complexity issues.
Lines of code per function/class
This basic metric counts the number of lines in a function or class. While a high LOC can indicate potential complexity, it’s not a definitive measure. Sometimes, a single line of complex logic can be more problematic than ten lines of simple code.
Maintainability index value
This composite metric highlights a codebase’s readability and maintainability based on factors like lines of code, cyclomatic complexity, and Halstead metrics. A lower maintainability index value suggests higher complexity because it reflects the difficulty of maintaining and understanding the codebase.
Cognitive complexity
Cognitive complexity coding metrics assess code based on its readability and self-explainability. These metrics focus primarily on the human aspect: how mentally taxing it is to understand and work with a piece of code.
For example, a simple if statement with a straightforward condition is considered low in cognitive complexity because it’s easy to understand. In contrast, nested loops or deeply nested conditional statements increase cognitive complexity, requiring more mental effort to follow and comprehend.
Measuring code complexity for optimized software development
Next, let’s discuss some free and open-source tools that you can use to measure and improve code complexity:
SonarQube
SonarQube is a code analysis and quality tool that offers different metrics for measuring code complexity, including cognitive complexity, cyclomatic complexity, code smells, and maintainability rating. Its community edition is free and open source.
PMD
PMD is an open-source cross-language static analysis tool developers can use to analyze code written in different languages for complexity. It supports different metrics, including cyclometric complexity and tight coupling.
Lizard
Lizard is another cross-language complexity analyzer that supports a long list of programming languages, including C/C++, Java, TypeScript, Scala, and PHP. It can count lines of code without comments, cyclomatic complexity, and other metrics.
Radon
Radon is a free and open-source tool for analyzing the complexity of Python programs. It can calculate Halstead metrics, cyclomatic code complexity, maintainability index, and other metrics.
Strategies to reduce code complexity
Now that we have identified the pitfalls of complexity, let’s talk about strategies to mitigate it:
Identify high code complexity areas and fix
As a first step, identify areas in your codebase that require immediate attention. Here’s how to go about it:
- Use the open-source tools mentioned above to calculate values for different complexity metrics, like Mccabe code complexity.
- Refactor or rethink the source code areas that are the most complex.
- Rerun complexity analysis to check if the level of complexity was decreased.
- Developers should identify overly complex logic and suggest improvements during code review sessions.
Incorporate automated complexity analysis into your workflow
Automate complexity analysis and make it a part of your development workflow. For example, if you already have a CI/CD pipeline, you can add a step that performs this analysis. Automating complexity analysis offers benefits like:
- Early detection of potential complex issues.
- Consistent monitoring of code complexity across the codebase.
- Increased developer efficiency, as they can focus on fixing complexity issues rather than identifying them.
Adopt loose coupling and other essential design principles
Keep your modules loosely coupled to reduce interdependencies and complexity. Other design principles to consider are:
- High cohesion: Each module should have a clear and well-defined purpose.
- Single responsibility principle: Design modules or classes with only one reason to change.
- Don’t repeat yourself: Eliminate code duplication by writing reusable functions.
Prioritize continuous maintenance
Lastly, acknowledge that code complexity isn’t a one-time fix but an ongoing effort. Dedicate time for regular refactoring efforts to address complexity issues and reduce technical debt. You’re cultivating a culture of writing clean, readable, well-documented, and well-tested code within your organization.
Final word
Code complexity has repercussions for the entire organization, not just developers. Overly complex code can delay the release of new features and bug fixes, which can annoy customers and other stakeholders. This post covered everything you need to know to manage and reduce code complexity; we hope you’ve found it helpful.
Remember, clean code isn’t just a developer best practice—it’s a strategic advantage.
Frequently asked questions about code complexity
What are some valuable tools for measuring code complexity?
PMD, Lizard, SonarQube, and Radon are some tools for measuring different code complexity metrics.
What is the best metric for assessing code complexity?
The maintainability index is one of the most frequently used metrics for assessing code complexity. It uses a combination of different metrics, including Halstead volume and cyclomatic complexity.
How often should complexity be analyzed?
Developers should perform code complexity analysis before any change gets merged to the master branch.
Can refactoring reduce code complexity?
Yes, refactoring can reduce code complexity. For example, you can refactor variable names to make a function easier to read and, hence, less complex.
What is the difference between cyclomatic complexity and code complexity?
Cyclomatic complexity is a metric that measures the complexity of a program’s control flow based on the number of independent paths through the source code. On the other hand, code complexity is a broader concept that includes several aspects, including a program’s structure, readability, and expressiveness.
