How to measure technical debt ratio: A guide for engineering leaders
A systems approach to quantifying code health and developer experience
Taylor Bruneaux
Analyst
Engineering leaders know technical debt slows teams, but most struggle to measure it in a way that captures the real impact. They rely on gut feeling, anecdotes, or isolated code metrics that miss the broader picture. The question isn’t whether technical debt exists, it’s how to quantify it in a way that connects code health to developer experience and organizational outcomes.
What is technical debt? Technical debt (sometimes called tech debt or technical dept) refers to the implied cost of future reworking required when teams choose quick solutions over better approaches that take longer. Like financial debt, tech debt accumulates “interest” over time, making every subsequent change more expensive and time-consuming.
In recent years, research on technical debt has moved beyond code quality to encompass the social and organizational conditions that create it. Debt is now best understood as a systems property, the cumulative gap between how software ideally should work and how it actually does in day-to-day development. That gap carries a cost: time spent navigating friction, fixing regressions, or reworking design decisions.
What causes technical debt? Technical debt arises from multiple sources: rushed deadlines that force shortcuts, evolving requirements that outpace refactoring, lack of documentation, outdated dependencies, inconsistent coding standards, and organizational silos that prevent holistic decision-making. In DevOps environments, technical debt in DevOps workflows can manifest as fragile CI/CD pipelines, manual deployment steps, or configuration drift across environments.
Despite its ubiquity, few organizations measure this debt consistently. The idea of a technical debt ratio offers a way to quantify sustainability, not to find a perfect number, but to provide a signal that connects the technical and human sides of engineering. In our research, we’ve found that teams able to measure and communicate debt can make better tradeoffs between velocity and sustainability.
Defining and calculating technical debt ratio
How to measure technical debt starts with understanding the technical debt ratio. At its simplest, technical debt ratio (TDR) expresses the relationship between the cost to remediate debt and the cost of building the system in the first place.
TDR = (Remediation Cost / Development Cost) × 100
A team with a TDR of 10 percent would need to spend one hour fixing accumulated issues for every ten hours of new work. It’s not a precision measure, but it helps quantify the “interest” teams pay on past decisions. For measuring technical debt ratio on new code, teams can track the ratio of bug fixes and refactoring commits to feature commits within recent sprints.
Teams with higher TDRs consistently report friction in feedback loops, elevated cognitive load, and reduced flow. These are not isolated symptoms. They map directly to the four dimensions of the DX Core 4 framework: velocity, quality, cognitive load, and feedback loops, which helps teams measure and improve developer productivity.
Connecting TDR to the DX Core 4
The DX Core 4 describes the core experiences that shape developer productivity. Technical debt influences each dimension in different ways.
Debt slows velocity by adding overhead to every change. It reduces quality by embedding fragility into the codebase. It raises cognitive load as developers spend more time deciphering dependencies or working around inconsistencies. And it weakens feedback loops by lengthening builds, tests, and deployment cycles.
When viewed through this lens, technical debt ratio becomes more than a code metric. It’s an experience metric. It quantifies how historical tradeoffs, organizational silos, or process shortcuts accumulate into friction that shapes developers’ daily work.
How to measure tech debt
Precise TDR calculations are rarely possible, but approximate measures, paired with developer perception data, can reveal meaningful trends. Following our approach using the Developer Experience Index, organizations can integrate both system-level and perceptual measures.
Technical debt metrics and tech debt metrics that work in practice include:
System-based technical debt metrics:
- Ratio of refactor time to feature time
- Code churn and file volatility
- Dependency age and security vulnerabilities
- Build and test latency increases
- Cyclomatic complexity trends
- Code coverage gaps
Perceptual technical debt measurement:
- Developer satisfaction with code clarity
- Confidence in making changes
- Perceived friction in understanding legacy systems
- Time spent navigating technical constraints
Technical debt examples
Technical debt examples that teams commonly track include: outdated framework versions requiring migration, duplicated code across services, missing test coverage in critical paths, manual deployment steps that should be automated, inconsistent API patterns, and documentation gaps that slow onboarding.
The combination of these perspectives (how systems behave and how developers experience them) creates a more complete picture. Features like our workflow analysis and team dashboards are designed to surface exactly this kind of insight: not just how much debt exists, but how it feels to those working within it.
Process and tools to measure technical debt
Effective technical debt measurement requires combining automated tooling with human insight. In our work with engineering organizations, we’ve found these approaches most valuable:
- Static analysis tools for code quality metrics
- Version control analysis for code churn and complexity trends
- Developer surveys like DevSat for perceptual measures
- Sprint analytics to track refactor vs. feature work ratios
- Incident tracking to identify debt-related failures
- Build and test monitoring to catch performance degradation
What matters most isn’t the precision of any single metric, but the combination of signals that reveal where technical debt creates the most friction for teams.
From technical debt to experience debt
In research on technical debt and developer morale, a clear pattern emerges: debt doesn’t just slow teams down. It drains energy, motivation, and autonomy. Over time, it becomes part of the culture, something developers learn to work around instead of fix.
In modern DevOps environments, this debt extends beyond code. It shows up in fragile CI/CD pipelines, manual deployment steps, configuration drift, unreliable tests, and gaps in observability or security. These aren’t isolated issues—they’re forms of friction embedded in the delivery process itself.
When teams normalize these inefficiencies as “just how things work,” the debt compounds. Every manual step or unstable process adds cognitive load and frustration, slows feedback loops, and erodes confidence in the system.
Viewed through a developer experience lens, technical debt becomes experience debt, or the accumulated cost of neglecting the environment in which developers work. Each workaround or friction point adds to that interest, making future improvements harder and more expensive to achieve.
AI-assisted engineering and the new shape of debt
The rapid adoption of AI-assisted development tools adds a new layer to the conversation. Our research shows that AI can both accelerate productivity and accelerate the accumulation of debt.
AI helps teams write and modify code faster, but it also changes the distribution of cognitive effort. Developers may integrate AI-generated code they don’t fully understand, leading to fragile or inconsistent patterns. In many cases, the code “works,” but its intent and maintainability are unclear. The result is a shift from visible debt (code smells, bugs, outdated dependencies) to latent debt: complexity that hides behind automation.
This creates a paradox. AI may temporarily reduce the apparent cost of remediation, lowering short-term TDR, while increasing the long-term interest rate of maintenance.
One way to conceptualize this is an AI-adjusted debt ratio:
TDR(AI) = (Remediation Cost + AI-Introduced Debt) / (Development Cost - AI-Acceleration Benefit) × 100
While still experimental, this framing reflects the tradeoffs we’ve observed in our AI Impact Analysis. AI introduces new forms of technical debt (pattern drift, inconsistency, and lack of context) that may not surface until systems evolve. The challenge for leaders is to see beyond short-term speed gains and build processes that ensure AI-generated code remains explainable, consistent, and maintainable.
In this sense, AI doesn’t just increase productivity. It amplifies the importance of developer experience as the primary safeguard against entropy.
What leaders can do next
Leaders measuring developer productivity and managing technical debt ratio should begin by tracking both the measurable and the perceptual. Combine static analysis with the “voice of the developer.” Visualize debt through the dimensions of the DX Core 4 to see where it slows feedback, inflates cognitive load, or undermines quality.
Crucially, account for AI-introduced debt explicitly. Faster code generation does not necessarily mean lower debt. The cost simply shifts from immediate friction to future maintainability. Organizations looking to measure AI’s impact need frameworks that capture both acceleration and accumulation.
Finally, treat refactoring as an investment in experience, not just code health. Reducing debt improves flow, clarity, and satisfaction, outcomes that compound into long-term velocity. Understanding how engineering allocation balances new features against maintenance work is essential for sustainable delivery.
When measured through a systems lens, technical debt ratio becomes a kind of organizational vital sign. It reflects how human, technical, and increasingly AI-driven forces interact over time. The goal isn’t to eliminate debt, but to understand how it grows, how it feels, and what it tells us about the experience of building software. Tracking the right software development metrics can help engineering leaders make this invisible work visible and actionable.
Frequently asked questions About technical debt
What is tech debt?
Tech debt meaning refers to the implied cost of rework caused by choosing quick, expedient solutions now instead of better approaches that take more time upfront. Like financial debt, it accrues “interest” that makes future changes progressively more expensive.
How do you measure technical debt?
Measuring technical debt combines quantitative metrics (code complexity, churn rate, refactor-to-feature ratios) with qualitative measures (developer surveys, confidence in making changes). The technical debt ratio provides a high-level indicator:
TDR = (Remediation Cost / Development Cost) × 100
Specific tech debt metrics reveal where friction occurs.
What is a good technical debt ratio?
While there’s no universal threshold, our research suggests teams with TDR below 5 to 10 percent maintain healthy velocity. Ratios above 20 percent typically indicate systemic issues requiring strategic intervention. Context matters: startups in rapid prototyping mode may temporarily accept higher ratios than mature products.
What is technical debt in DevOps?
Technical debt DevOps refers to accumulated shortcuts in deployment pipelines, infrastructure automation, and operational practices. Examples include manual deployment steps, configuration drift, fragile CI/CD pipelines, and inadequate monitoring, issues that compound over time and slow delivery.
What causes technical debt?
Technical debt originates from multiple sources: time pressure forcing shortcuts, evolving requirements, inadequate documentation, outdated dependencies, inconsistent standards, lack of refactoring time, knowledge silos, and organizational constraints. Both intentional tradeoffs and unintentional oversights contribute.
How do you calculate technical debt ratio on new code?
For new code, track the ratio of remediation work (bug fixes, refactoring commits) to feature development within recent sprints. This reveals whether new development is adding to or reducing overall debt burden.
What are the best technical debt metrics?
Some effective tech debt metrics include: code churn rate, cyclomatic complexity trends, test coverage gaps, time-to-fix for bugs, developer-reported friction, refactor-to-feature work ratios, dependency age, and build/test performance degradation. However, combining multiple signals and understanding the full picture of developer productivity provides better insight than any single metric.