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Want to know what silently drains your engineering team's productivity? Complex code might be the hidden culprit.
Research has shown that complex code may require 250 - 500% more maintenance time than simple code of the same size. This slows down developers, especially if they need to spend significant time reading and understanding existing code rather than writing new features. When codebases become overly complex, this time investment grows exponentially — leading to slower delivery and increased bugs. Complex code doesn't just slow teams down - it can lead to frustration, decreased job satisfaction, and ultimately burnout, especially for newer team members or those from non-traditional backgrounds.
In this guide, we'll explore what makes code complex, examine its impact on developer productivity, and share data-driven strategies for maintaining cleaner, more maintainable codebases.
Whether you're an engineering leader looking to improve team velocity or a developer seeking to write more sustainable code, you'll learn practical approaches to reduce complexity and boost development efficiency.
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In the IEEE standard computer dictionary (Geraci et al. 1991), code complexity is defined as “the degree to which a system or component has a design or implementation that is difficult to understand and verify”.
Since then, the software engineering community has invested decades of research into code complexity because it fundamentally shapes code quality. A systematic review conducted on the topic of code complexity revealed that code complexity influences the ability of software engineers to progress software development — in particular the maintainability and defect proneness of code.
Practically however, it’s important for engineering leaders to not get too deep in the weeds of how specifically code complexity is defined or measured, but instead the business outcomes it impacts. Code complexity isn't just about difficult-to-read code — it fundamentally impacts your team's ability to:
There are typically 3 types of complexity we’ve found to be most commonly referred to in the industry:
Developed by Thomas J. McCabe, Sr. in 1976, cyclomatic complexity is a metric used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code.
In simpler language, it measures the number of different paths that code can take when it runs. Think of it like counting the number of different ways you can walk through a maze — more paths mean more complexity.
While cyclomatic complexity tells us how tricky code is to test, cognitive complexity reveals something equally important — how easy it is for developers to read and understand the code at a glance. Think of it as measuring the mental effort needed to follow the code's logic.
High cognitive complexity often signals potential trouble ahead. Like a puzzle with too many pieces, it's more likely to hide bugs which makes it risky to modify. This creates a domino effect: developers spend more time trying to understand and fix the code, which slows down the entire development process. The key difference to cyclomatic complexity is that it tries to measure how difficult code is to understand from a human perspective, rather than just counting paths.
Time complexity helps us understand how long an algorithm takes to run as its input grows. It's like measuring how much longer it takes to count a bigger stack of cards — are we talking about a linear increase (twice the cards = twice the time), or does it get exponentially slower?
Instead of measuring actual clock time, we count the number of basic operations an algorithm must perform. This gives us a hardware-independent way to evaluate how long algorithms take to run. measure since different computers run at different speeds.
For instance, while one computer may process 1 million operations per second and another only 500,000, the underlying pattern of how many operations are needed remains constant. You may also recall the dreaded Big O notation from computer science classes — this is very much related to time complexity! Big O notation gives us a mathematical way to compare algorithm efficiency regardless of the hardware they run on.
Code complexity presents significant challenges for both functionality and maintainability in software development. Understanding these challenges and their consequences helps teams make strategic decisions to ensure the long-term success of their projects.
As Robert C. Martin, co-author of the Agile Manifesto, astutely observed, "The ratio of time spent reading versus writing is well over 10 to 1." This insight reveals a clear reality: developerstypically spend far more time understanding existing code than writing new code. When we introduce unnecessary complexity, we amplify this imbalance and slow down the development process.
The relationship is clear: reducing complexity can accelerate development. To better understand the impact of high code complexity, let's examine the top 3 challenges we consistently observe:
A smooth flow of work is essential for engineering productivity. When codebases are clean and well-structured, teams can focus on delivering value - shipping features, fixing bugs, and deploying updates efficiently.
However, increasing code complexity can disrupt this flow in subtle ways. What begins as slightly longer task completion times can evolve into systemic slowdowns, with developers spending more time deciphering code than writing it. Code reviews become more time-consuming, and team momentum gradually diminishes.
Complex codebases don't just slow down development - they make the work inherently more challenging. Understanding code interactions and potential impacts of changes becomes increasingly difficult, affecting both output and team morale.
As code becomes more complex, it becomes harder to read and understand. Research shows that on average developers may spend up to 70% of their time trying to comprehend code. This increased cognitive load means developers spend more mental energy figuring out how the code works rather than adding new features or improvements.
In the long run, complex code drives up both time and costs. Development teams face a cascade of challenges: new developers struggle to get up to speed, experienced developers get pulled into troubleshooting instead of strategic work, and the team deploys code less frequently. It also introduces significant [key person risk](https://www.adventone.com/key-man-risk-why-depending-on-a-single-it-expert-is-risky-business/#:~:text=In essence%2C if access to,work environment and managerial challenges.) where only one or two developers truly know how the code works.
Research has shown there is a negative relationship between Code Complexity and Software quality (Pearson’s Coefficient of -0.88!). Complex code is harder to test thoroughly, which often leads to bugs slipping through. This creates a domino effect: system performance suffers, reliability drops, and ultimately users have a worse experience with your software.
This is why measuring code complexity isn't just a nice-to-have — it's an essential practice for maintaining healthy, efficient software systems. If it isn’t enough for your team’s sake alone, it also has a strong impact on the overall customer experience. Great businesses aren’t built on top of a poor customer experience!
Managing code complexity ultimately is a cultural norm that needs to be set within development teams. That being said, there are things that you can do to make it easier for everyone to write simpler code.
Clear, consistent coding standards act as a foundation for reducing complexity. When development teams align on naming conventions, styles, and patterns, they create a shared language that makes code more intuitive to understand and maintain. This alignment means developers can focus their energy on building new features rather than decoding unfamiliar code patterns.
To implement effective standards:
Large, multi-purpose functions are like Swiss Army knives - they do many things, but often at the cost of clarity. Breaking these functions into smaller, focused methods makes code more intuitive and speeds up development. This is the core premise refactoring which improves overall code design and internal quality.
Consider a user registration system: instead of one massive function that handles everything, break it into targeted methods:
validateUserInput: Check email, password, and profile datacreateUserProfile: Set up the core user recordsetupUserPreferences: Initialize default settingssendWelcomeEmail: Handle onboarding communicationThis modular approach means developers can quickly understand and modify specific functionality without wading through unrelated code. This leads to faster development, easier testing, and more maintainable code that scales with your team's growth.
Data-driven practices help engineering teams optimize their efforts around code complexity. By establishing clear benchmarks for code quality, complexity, and performance, teams can identify and address issues systematically rather than reactively.
Key metrics to monitor include:
Consider integrating automated metrics collection into your CI/CD pipeline for continuous monitoring. This proactive approach helps teams spot emerging issues before they impact productivity.
When tackling code complexity, it's important to keep the bigger picture in mind. While cleaner code is valuable, what really matters is how it impacts your team's ability to deliver value. Complex code often shows up as slower lead times or longer cycle times — after all, it takes developers longer to ship features when they're navigating through intricate code paths.
But here's the thing: reducing complexity is only meaningful if it helps your team perform better. If your complexity metrics improve but your team isn't shipping faster or more reliably, you might be optimizing the wrong thing. That's why we recommend tracking a comprehensive set of developer productivity metrics, like those from DORA and SPACE frameworks, alongside your complexity measures.
DORA metrics have evolved to be an accepted industry benchmark for developer productivity, as it is based on research spanning over 39,000 professionals across organizations of all sizes and industries. These metrics have proven to be reliable predictors of organizational performance. Over time, these metrics have evolved, leading to updates and the introduction of a fifth metric in 2024:
You can read more about what we measure and why here — other relevant frameworks include SPACE (Satisfaction, Performance, Activity, Communication, and Efficiency) and Developer Experience (DevEx).
To holistically measure developer productivity beyond code complexity, teams can rely on Multitudes, an engineering insights platform designed for sustainable delivery.
Multitudes integrates seamlessly with tools like GitHub and Jira, offering a comprehensive view of your platform’s technical performance, operational health, and team collaboration.
With Multitudes, you can:
By leveraging Multitudes, platform teams can spend less time on data collection and more on using actionable insights to improve platform performance
Our clients ship 25% faster while maintaining code quality and team wellbeing.
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