IT-03
Technical Debt Items Accumulating
Issue Type Distribution
Default severity: medium
Product BacklogMetricsQuality
What it detects
The proportion of technical debt, refactoring, or enabler issues is growing over consecutive sprints — the team is spending increasing capacity on maintenance rather than feature delivery. Healthy teams maintain technical debt below the configured threshold.
Detection formula
tech_debt_types = config.it.tech_debt_types // e.g. \[\'Tech Debt\', \'Refactor\', \'Enabler\', \'Spike\'\] FOR each_sprint S: td_ratio(S) = COUNT(tech_debt_types, sprint=S) / COUNT(all_issues, sprint=S) IF AVG(td_ratio, last config.it.trend_window_sprints) > config.it.tech_debt_threshold // default: 0.20 THEN FLAG excessive_tech_debt IF linear_regression(\[td_ratio\]).slope > 0 THEN FLAG td_growingExamples in practice
- A team shows the proportion of technical debt, refactoring, or enabler issues is growing over consecutive sprints while tech debt types is set to \[\'Tech Debt\',\'Refactor\',\'Enabler\',\'Spike\'\].
- Example signal: The proportion of technical debt, refactoring, or enabler issues is growing over consecutive sprints — the team is spending increasing capacity on maintenance rather than feature delivery.
Suggested response
Rebalance issue-type mix and debt trends so quality signals stay intentional.
Coaching playbook
Symptom
The proportion of technical debt, refactoring, or enabler issues is growing over consecutive sprints — the team is spending increasing capacity on maintenance rather than feature delivery. Healthy teams maintain technical debt below the configured threshold.
Why it matters
When "Technical Debt Items Accumulating" keeps appearing, the team is signalling a repeatable process gap. Left unexamined, the pattern hides where work really stalls and makes improvement metrics harder to trust.
What you can achieve
Rebalance issue-type mix and debt trends so quality signals stay intentional.
Facilitation questions
- What system change would stop "Technical Debt Items Accumulating" from firing again?
- What do the cited issues have in common — same root cause or same workaround?
- Who owns the two-week experiment and how will we verify on the next import?
Run this rule against your own tracker data with Flow Analyzer.