What is technical debt?

Types of technical debt:

• Code debt (untidy, redundant, or complex code).

• Architectural debt (inappropriate or outdated structures, tight coupling).

• Test debt (missing or unstable tests).

• Build/deploy debt (manual or fragile pipelines).

• Documentation debt (missing or outdated documentation).

Economic model:

• Principal (main debt): Effort required to implement the better solution now.

• Interest: recurring additional costs, e.g., longer development time, more defects.

• Interest rate: increases in areas of code that are changed frequently ("hotspots").

Intent & quality of the decision (Fowler quadrant):

• Intentional vs. unintentional × prudent vs. reckless → four types.

• "Intentional-prudent" can be useful (conscious market launch despite compromise).

• "Unintentional-reckless" almost always leads to problem interest rates.

❌ "Technical debt = bugs" – Bugs are defects; debt affects the maintainability and evolvability of the system.

❌ "Tools provide the whole truth" – SonarQube/SQALE primarily estimate maintainability debt (code smells), but often leave architecture, test, or build debt invisible.

❌ "Rewriting everything solves it" – Big bang rewrites often only shift debt; incremental approaches (e.g., strangler pattern) are less risky.

Business impact:

• A large-scale study of 39 commercial codebases showed that low-quality code contained up to 15 times more defects and that issues took an average of 124% longer to resolve.

• The authors emphasize: correlation, not causation – results apply to this sample, not universally.

• Other case studies report mixed or even no effects on throughput times, depending on the context and measurement method.

Conclusion: Technical quality is business-relevant, but the effect is context-dependent – organizations must carry out their own measurements.

A financial services provider identified a hotspot service (frequently changed, high complexity value). The result: long throughput times, errors in releases. Hotspot analysis and investigation of temporal coupling revealed dependencies. The team extracted a smaller component, added tests and CI checks. The result: shorter cycle times and a lower error rate in the affected value stream.

A. Make visible & evaluate

• Debt register (items with principal/interest), heat maps for hotspots, architecture reviews.

• Use tools (SonarQube, SQALE) – but only as a partial indicator.

B. Prioritize according to economics

• Prioritize debt according to cost of delay (CoD)/WSJF and hotspot score.

• Assign repayment plans to conscious debts.

C. Repayment in flow

• Continuous refactoring during ongoing operations.

• Explicitly plan architecture enablers.

• Use strangler patterns for legacy systems.

D. Quality routines & definition of done

• TDD, automated testing, CI/CD, code reviews.

• Explicitly extend definition of done with refactoring/testing.

E. Measure impact

• Combination of maintainability metrics (e.g., Sonar) and flow metrics (lead/cycle time, defect density, change failure rate).

• Important: Ratings are indicators, not absolute judgments.

1.        Create a baseline: debt register + hotspot map.

2.        Prioritize economically: WSJF × hotspot score → top items.

3.        Repayment in cadence: fixed refactoring slots or enablers in each PI/sprint.

4.        Manage architecture debt separately: cuts, dependencies, observability.

5.        Measure impact: Focus on outcomes (e.g., shorter cycle time, fewer defects), not just "hours invested."

We see technical debt as a strategic risk in the portfolio. Our approach:

• Create visibility (debt register, hotspot analysis).

• Prioritize economically (CoD, WSJF).

• Secure binding capacity (refactoring/enabler slots).

• Repay and measure incrementally.

Where organizational policies increase debt (e.g., rigid approvals, lack of platform capabilities), we combine our work with Organizational Debt, Living Strategy® (prioritization, strategy sprints), and Living Transformation® (3-month cadence, Capa/Prio events). This results in holistic debt reduction that combines technology and organization.

Related terms & sources

• Ward Cunningham: Debt Metaphor (1992).

• Martin Fowler: TD Quadrant.

• SEI/CMU – Managing Technical Debt (Kruchten, Nord, Ozkaya).

• Systematic Reviews (Li, Avgeriou et al., 2015).

• Hotspots & Temporal Coupling (Tornhill/CodeScene).

• SonarQube/SQALE – Maintainability Debt Ratio.

• Empirical business impact studies – correlation between quality, defects & cycle time; consider context.