What Is Measure Technical Debt?

To measure technical debt is to quantify the accumulated cost of deferred maintenance, shortcuts, and suboptimal design decisions within a software system using a combination of automated metrics and qualitative evaluation. Measurement translates the abstract concept of "debt" into concrete numbers that engineering and business leaders can act on. Without measurement, technical debt remains an invisible drag on productivity that is easy to ignore until it triggers a crisis.

Technical debt is one of the most discussed yet least measured aspects of software engineering. Teams acknowledge it exists, complain about it in retrospectives, and occasionally carve out time to address it, but rarely quantify it in ways that support strategic decision-making. This gap between awareness and measurement creates a cycle where debt grows unchecked because no one can demonstrate its true cost.

A 2023 Stripe developer survey found that the average developer spends 33% of their time dealing with technical debt and maintenance. Translated to salary costs across an engineering organization, that percentage represents millions of dollars annually. Measuring technical debt converts that vague awareness into specific dollar amounts, time estimates, and risk scores that justify investment in remediation.

Measurement also enables comparison over time. Without a baseline, teams cannot determine whether their debt is growing, shrinking, or holding steady. Ongoing measurement creates a trend line that reveals whether remediation efforts are working or whether new debt is accumulating faster than old debt is being paid down. This visibility is foundational to any sustainable technical debt tracking practice.

Measuring technical debt involves both automated and manual approaches. Automated tools scan the codebase and calculate metrics such as code duplication percentage, cyclomatic complexity, dependency age, test coverage gaps, and static analysis violation counts. These metrics serve as proxies for debt, each representing a dimension of code that will require future effort to maintain or improve.

The manual dimension involves engineering judgment. Architects and senior developers assess areas that tooling cannot easily quantify: design decisions that constrain future development, infrastructure patterns that do not scale, and knowledge silos where only one person understands a critical system. Many teams use a debt register, a catalog where engineers log debt items with severity ratings, estimated remediation effort, and the business systems affected.

Converting debt measurements into a common unit, typically developer hours or estimated cost, is what makes the data actionable. For example, a team might calculate that their test coverage gaps represent 120 hours of remediation work, while their outdated dependency tree would take 80 hours to modernize. These numbers allow leadership to compare debt remediation against feature work using the same resource currency. For detailed approaches to surfacing these numbers, see this resource on technical debt visibility.

Static analysis platforms like SonarQube provide a "technical debt" metric measured in estimated remediation time. CodeScene uses behavioral analysis of version control history to identify hotspots where debt and change frequency intersect, highlighting the most expensive debt. Dependency scanning tools like Dependabot and Renovate track outdated packages and known vulnerabilities. Glue takes a broader view by connecting codebase-level debt indicators to project delivery context, helping teams understand not just where debt exists but how it affects their ability to ship on time.

The most effective measurement strategies combine multiple tools and methods. Automated scanning provides breadth and consistency. Developer surveys capture debt that tooling misses. Incident post-mortems reveal debt that has already caused production impact. Bringing these data streams together creates a comprehensive view that no single tool can provide on its own.

What units should technical debt be measured in?

The most practical unit is estimated developer hours required for remediation, because it translates directly into resource planning and cost analysis. Some tools express debt in monetary terms by multiplying hours by average developer compensation. Others use severity scores or risk ratings. The best approach depends on your audience: financial units resonate with executives, while hours resonate with engineering leads planning sprint capacity.

How do you measure technical debt that is not visible in the code?

Architectural debt, process debt, and knowledge debt often do not show up in static analysis. To capture these, teams use developer surveys, architecture review sessions, and incident retrospectives. A common technique is to ask engineers to rate the difficulty of making changes in different parts of the system on a numeric scale. Aggregating these ratings across the team reveals areas where hidden debt is creating the most friction.

Is there a threshold for acceptable technical debt?

There is no universal threshold because acceptable debt levels depend on the product's maturity, growth rate, and risk tolerance. A startup in rapid growth mode may tolerate higher debt levels than a healthcare platform where reliability is critical. The key is to measure debt consistently, set context-appropriate targets, and monitor trends. If debt is growing faster than the team's capacity to address it, that signals a problem regardless of the absolute number.