Measure technical debt with an itemized inventory, not a standalone score. For each weakness, record what it affects, how it was identified, the estimated effort to fix it (principal), and any recurring extra effort it causes while it remains (interest). State the scope and assumptions: a debt estimate is useful for a particular decision, not a universal measure of a system’s health.
What a technical-debt count should tell you
A useful measurement answers two separate questions: what would it take to correct the current weakness, and what is it costing the team to leave it in place? Those answers are related, but they are not interchangeable.
- Principal: the estimated effort or cost to remediate a debt item. A static-analysis method, for example, may estimate corrective-maintenance effort for specified code weaknesses.
- Interest: recurring extra effort or inefficiency associated with leaving that item unresolved, such as additional maintenance work. Track it separately, and only quantify it when you have a defensible way to observe it.
A principal estimate by itself does not show ongoing impact. Conversely, an observed maintenance burden does not say how much work remediation will require.
How to measure technical debt in practice
- Set the boundary. Name the system and release or branch, the artifacts in scope, and the concerns being assessed. Keep unlike scopes separate or label them clearly.
- Identify debt items using a stated method. For code, that might be a documented structural-quality assessment, code or architectural smells, or another specified approach. If requirements are in scope, identify ambiguity, incomplete or missing specifications, and unmet needs.
- Estimate principal item by item. Record the effort or cost to correct each item, along with the estimate’s assumptions. For example, the CISQ Technical Debt Standard describes static-analysis estimates for correcting weaknesses covered by its code-quality standards at release. That is a defined way to estimate principal for covered weaknesses, not a measure of every kind of debt.
- Record interest separately. Note recurring consequences, such as extra maintenance effort, when the team can observe them reliably. If a consequence matters but cannot be quantified credibly, describe it rather than inventing a number.
- Keep evidence with each item. Record the affected component, identification method or metric, date, estimate, and assumptions. An aggregate is only as useful as the item-level information behind it.
- Use the inventory to make a decision. Compare remediation effort with observed recurring impact and the project’s needs. A ranking can help direct attention, but its value depends on the quality and relevance of the underlying evidence.
Choose a method that matches the debt you mean
Methods and tools do not all identify or quantify the same thing. Compare their scope, metrics, identification approach, validation, and availability before treating their outputs as comparable. A 2021 review describes variation in technical-debt measurement tools; it is a review, not a current product catalog: Avgeriou et al., “An Overview and Comparison of Technical Debt Measurement Tools”.
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| Method family | What it can help measure | What to check |
|---|---|---|
| Standards-based static analysis | Estimated corrective-maintenance effort for specified code weaknesses. CISQ describes estimates for weaknesses covered by its code-quality standards at release (CISQ Technical Debt Standard). | Which weaknesses are covered, what effort assumptions are used, and whether the result measures principal only or also ongoing effects. |
| Code- or architecture-smell and quality-based approaches | Research has explored smells, software-quality properties, and aggregated principal or interest measures (2020 empirical study; Sas and Avgeriou, 2023). | Which artifact level is assessed, how metrics are combined, and what validation supports the estimate or index. |
| Requirements-debt models | Requirements-related problems and their downstream effects on design and implementation. A 2024 study presents a conceptual model for examining existing approaches (Perera et al., “Modelling the quantification of requirements technical debt”). | Whether the approach covers the requirements items, feedback, downstream consequences, and measurements relevant to your decision. |
These are different measurement families, not interchangeable ways to obtain a single agreed score. Google Research’s 2023 discussion of defining, measuring, and managing technical debt is also useful context for keeping the definition and measurement approach explicit: Jaspan and Green.
Include requirements debt when it is in scope
Technical debt is not limited to code. Requirements debt can arise when requirements are ambiguous, inadequate, missing, or unmet. Those issues can carry consequences into design and implementation, so a code-only count will not represent them.
Perera et al.’s 2024 study reports that there is no commonly agreed definition or quantification approach for requirements debt. It also identifies gaps in metrics for concepts including interest constituents and priority. That means a team should not imply that every consequential requirements problem already has a reliable numerical measure. The study’s conceptual model is a way to examine existing approaches, not proof of a universal scoring method.
Use empirical findings as clues, not a formula
Consider principal and interest together
A 2020 empirical study found that classes with similar levels of technical-debt principal tended to have similar interest. In that study, aggregated principal or interest measures identified nearby artifacts better than isolated metrics. This supports examining combined evidence when ranking work; it does not establish that aggregation is better for every codebase or organization. Read the study.
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Use size and coupling as context
The same study reported that high values for properties such as size and coupling were associated, in most cases in the studied artifacts, with higher principal. These properties can inform a refactoring review, but they are not a universal formula for debt or priority.
Do not assume one sustainability breakpoint
An industrial validation of the Technical Debt Breaking Point framework reported correlation between its results and experts’ opinions on module sustainability, and described its usefulness for ranking components by maintenance difficulty. It illustrates one way to reason about accumulated interest and sustainability; it does not supply a breakpoint that applies to every system. Ampatzoglou et al., “A Framework for Managing Interest in Technical Debt: An Industrial Validation”.
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Check what an index actually validates
Sas and Avgeriou’s 2023 architectural-debt article proposed a machine-learning index based on architectural smells. Its abstract reported that none of the approaches it reviewed met all three criteria of full automation, free availability, and thorough validation. That finding describes the approaches reviewed in that publication context; it does not establish the current availability or validation status of every tool. Read the article.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the number explainable
Before sharing a total, preserve the inventory it came from. A reader should be able to see what was counted, where it was found, how the estimate was produced, and which effects were observed rather than inferred. If two reports cover different artifacts, definitions, or methods, their totals should not be presented as directly comparable without explaining those differences.
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