To measure technical debt, inventory specific weaknesses, estimate the effort to fix each one, and separately record any recurring extra work they cause. Make the scope and method explicit: there is no single universal debt score, and a total is only useful when readers can trace it back to the items and evidence behind it.
What a technical-debt measurement should tell you
A useful measurement answers two different questions. Principal is the estimated effort or cost to correct a weakness that exists now. Interest is the recurring extra effort or inefficiency the weakness causes while it remains. A remediation estimate alone measures principal; it does not tell you how much the item is slowing maintenance or development.
Technical debt also extends beyond code. Requirements debt can include ambiguous, inadequate, missing, or unmet requirements. Its effects may flow into design and implementation, so a code-only assessment should be labeled as such rather than presented as a complete account of a system’s debt.
How to measure technical debt in practice
- Set the boundary. Name the system, release or branch, artifact types, and quality or requirements concerns being assessed. Keep unlike scopes separate or label them clearly.
- Identify specific items. For code, document the chosen method—for example, structural-quality weaknesses, code smells, or architectural smells. If requirements are in scope, record issues such as ambiguity, incompleteness, missing specifications, and unmet needs.
- Estimate principal item by item. Record the estimated remediation effort or cost for each item, along with the assumptions behind the estimate. CISQ’s Technical Debt Standard describes static-analysis estimates for correcting weaknesses covered by its code-quality standards at release. That is a defined approach to a specified set of code weaknesses, not a measure of every kind of debt.
- Track interest separately when you can support the estimate. Capture observable recurring consequences, such as added maintenance effort or inefficiency. State how the team observed them; do not turn an unsupported impression into a precise figure.
- Preserve the evidence. For each item, keep its affected component, metric or review method, date, estimate, and assumptions. A roll-up should remain traceable to this inventory.
- Use the result to make a decision. Compare remediation effort with recurring impact and project needs. An aggregate or ranking is only as dependable as the method and data supporting it.
Choose a method that matches the scope
Measurement approaches do not all assess the same artifacts or concepts. Compare their coverage, assumptions, validation, automation, and availability before treating their outputs as interchangeable.
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| Approach | What it can measure | What to check |
|---|---|---|
| Standards-based static analysis | Estimated corrective-maintenance effort for specified structural weaknesses covered by a standard. | Which weaknesses are included and what effort assumptions are used. CISQ describes estimates for code-quality weaknesses at release in its Technical Debt Standard; this is not a comprehensive measure of all debt types. |
| Code- or architecture-smell and quality-based methods | Research has explored smells, software-quality properties, and aggregated measures of principal or interest. | Whether the assessment is at code or architectural level, how metrics are combined, and what validation supports the result. See the 2020 empirical study on principal and interest and the 2023 study proposing a machine-learning index based on architectural smells. |
| Requirements-debt models | Requirements-related issues and consequences that may extend into design and implementation. | Whether the model covers the relevant requirements items, user feedback, downstream effects, and available measurements. The 2024 requirements technical-debt model examines existing approaches and reports gaps in metrics for interest and other decision concepts. |
| Measurement tools | Tool-dependent metrics and identification methods; outputs vary across tools. | Terminology, scope, identification method, validation, automation, and availability. The 2021 tool comparison review describes variation among tools; it is not a current product catalog. |
Use the numbers carefully when prioritizing
Principal and interest provide complementary evidence, not a self-executing priority formula. An item that is expensive to fix may impose little recurring burden; a smaller repair may remove repeated friction. Consider the estimated remediation effort alongside observed impact and the needs of the project.
One 2020 empirical study found that classes with similar levels of principal tended to have similar interest. In the studied artifacts, aggregated principal or interest measures identified nearby artifacts better than isolated metrics. The study also found that high values for properties such as size and coupling were, in most cases, associated with higher principal. These findings can inform a ranking, but they do not establish a rule that applies to every codebase or organization. See the study’s account of the relationship between principal and interest.
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Likewise, an industrial validation of the Technical Debt Breaking Point framework reported correlation with experts’ opinions about module sustainability and an ability to rank components by maintenance difficulty. It illustrates one way to reason about accumulated interest; it does not supply a universal breakpoint for deciding when a system must be refactored.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a debt count cannot tell you by itself
There is no broadly agreed single quantification approach across the requirements-debt literature. A 2024 study reports no commonly agreed definition or way to quantify requirements debt, distinguishes it from code-related debt, and identifies consequences that can reach design and implementation. It also reports that some decision concepts—including requirements-debt interest constituents and priority—lack associated metrics. Important consequences therefore may not yet have a reliable measure.
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Tool outputs are similarly method-specific. A 2021 review found variation in terminology, metrics, identification, and measurement among technical-debt tools. A 2023 architectural-debt study proposed a machine-learning index based on architectural smells; its abstract reported that none of the approaches it reviewed met all three criteria of being fully automated, freely available, and thoroughly validated. That finding describes the approaches reviewed in that study, not the current availability of every tool.
For a broader framing of defining, measuring, and managing technical debt, see Jaspan and Green’s 2023 Google Research publication.
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