IA-01
Age Distribution — Backlog Aging Profile
Issue Age
Default severity: medium
Product BacklogPrioritizationRiskEffort
What it detects
Computes the full age distribution of all open issues in the backlog (not yet started) to identify the volume and proportion of stale backlog items. A healthy backlog has most items less than 60 days old; a stale backlog has a long tail of items 180+ days old that are never worked on. open_backlog = issues WHERE status IN config.workflow.initial_statuses
Detection formula
FOR each_age_bucket B in config.ia.age_buckets // default: \[0-30, 31-60, 61-90, 91-180, 181-365, 365+\]: bucket_count(B) = COUNT(open_backlog WHERE age_days IN B) bucket_sp(B) = SUM(sp, bucket) bucket_bv(B) = SUM(bv, bucket) IF bucket_pct(181+) > config.ia.stale_backlog_threshold // default: 30% of backlog > 180d THEN FLAG stale_backlog Report: age distribution histogram for issues, SP, and BV per bucketExamples in practice
- A team shows computes the full age distribution of all open issues in the backlog (not yet started) to identify the volume and proportion of stale backlog items while age buckets is set to \[0-30, 31-60, 61-90, 91-180, 181-365, 365+\]: bucket_count(B) = COUNT(open_backlog WHERE age_days IN B) b.
- Example signal: Computes the full age distribution of all open issues in the backlog (not yet started) to identify the volume and proportion of stale backlog items.
Suggested response
Age down old items: shorten time-in-status, clear blockers, and focus high-value ageing work.
Coaching playbook
Symptom
Computes the full age distribution of all open issues in the backlog (not yet started) to identify the volume and proportion of stale backlog items. A healthy backlog has most items less than 60 days old; a stale backlog has a long tail of items 180+ days old that are never worked on. open_backlog = issues WHERE status IN config.workflow.initial_statuses
Why it matters
When "Age Distribution — Backlog Aging Profile" 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
Age down old items: shorten time-in-status, clear blockers, and focus high-value ageing work.
Facilitation questions
- What system change would stop "Age Distribution — Backlog Aging Profile" 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.