method#Governance#Delivery#Architecture#Software Engineering

Technical Debt Management

A method for systematically identifying, assessing and repaying technical debt across organizational levels.

Technical Debt Management defines processes and decision rules to identify, prioritize, and reduce technical debt.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaOrganizational
Decision typeOrganizational
Organizational maturityIntermediate

Technical context

Integrations

Static code analysis tools (e.g. SonarQube)Issue trackers (e.g. Jira, GitHub Issues)CI/CD pipeline for measurement and regression checks

Principles & goals

Principles

Transparency: measure and report debt openlyOwnership: assign owners to debt itemsCost–benefit orientation: prioritize by risk and value

Value stream stage

Iterate

Organizational level

Enterprise, Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Short-term fixes without sustainability (growing debt)
Overhead from excessive governance
Misprioritization endangers product goals

Best practices

Keep debt items small and manageable
Schedule regular, timeboxed debt windows
Prioritize technical and product goals jointly

I/O & resources

Inputs

Code and architecture analysis reports
Backlog and issue data
Team capacity and business priorities

Outputs

Prioritized action list
Governance decisions and budget allocations
Metrics and reporting dashboards

Resources

Description

Technical Debt Management defines processes and decision rules to identify, prioritize, and reduce technical debt. It aligns technical assessment with product and governance goals and specifies ownership, metrics, and repayment strategies. The method covers tactical remediation and strategic roadmaps to sustain code quality and lower long-term costs.

✔Benefits

Reduced long-term maintenance costs
Improved release stability and predictability
Targeted investments in architecture and code quality

✖Limitations

Requires regular measurement and discipline
Potential conflicts between product and engineering goals
Not all debts are immediately quantifiable

Trade-offs

Metrics

Technical Debt Ratio
Ratio of estimated remediation cost to development cost.
Change Failure Rate
Share of failed releases attributable to technical debt.
Mean Time to Restore (MTTR)
Average time to resolve production issues.

Examples & implementations

Legacy module refactored after measurement

Team measured TD with SCA tool, prioritized component and planned refactor across two sprints.

Product-technology trade-off in roadmap meeting

Product team deferred a feature to enable urgent architecture improvements.

Governance rule for annual debt inventory

Organization established an annual debt inventory and KPI targets for control.

Implementation steps

Select and integrate metrics and tools

Create initial inventory of debt items

Define prioritization criteria and governance rules

Select pilot team for sprint-based repayment

Introduce reporting and regular reviews

⚠️ Technical debt & bottlenecks

Technical debt

Outdated libraries with security risks
Missing tests and CI coverage
Monolithic components without clear interfaces

Known bottlenecks

Legacy codeLack of capacityUnclear ownership

Misuse examples

Using debt measurement tools without calibration
Using refactoring as substitute for necessary architectural decisions
Using governance to shift blame for debt

Typical traps

Focusing on easily measurable rather than effective measures
Unclear definition of what counts as technical debt
No evidence of benefit after repayment

Required skills

Architecture knowledgeCode review and refactoring skillsStakeholder management and prioritization

Architectural drivers

Modularity and clear interfacesObservability to measure quality indicatorsTest coverage and automation

Constraints

• Limited development resources
• Budget constraints
• Regulatory requirements