IT-05
Bug-to-Feature Ratio — Quality Signal per Team
Issue Type Distribution
Default severity: medium
Product BacklogMetricsQuality
What it detects
Cross-team comparison of bug-to-feature ratio over the same period — identifies which teams have the highest quality debt relative to feature output. A team with bug_ratio significantly above the portfolio average is a quality risk node. portfolio_avg_bug_ratio = AVG(bug_ratio, all_teams, last N sprints)
Detection formula
FOR each_team T: team_bug_ratio(T) = AVG(bug_ratio(T), last config.it.trend_window_sprints) IF team_bug_ratio(T) > portfolio_avg_bug_ratio * config.it.team_outlier_multiplier // default: 1.5x THEN FLAG team T as quality_outlier Report: ranked table of teams by bug_ratio descendingExamples in practice
- A team shows cross-team comparison of bug-to-feature ratio over the same period while trend window sprints is set to 1.5x.
- Example signal: Cross-team comparison of bug-to-feature ratio over the same period — identifies which teams have the highest quality debt relative to feature output.
Suggested response
Rebalance issue-type mix and debt trends so quality signals stay intentional.
Coaching playbook
Symptom
Cross-team comparison of bug-to-feature ratio over the same period — identifies which teams have the highest quality debt relative to feature output. A team with bug_ratio significantly above the portfolio average is a quality risk node. portfolio_avg_bug_ratio = AVG(bug_ratio, all_teams, last N sprints)
Why it matters
When "Bug-to-Feature Ratio — Quality Signal per Team" 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 "Bug-to-Feature Ratio — Quality Signal per Team" 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.