Large-scale development environment with a linear priority system (P1–P4) and high bug volume during stabilization phases.
Priority labeling was inconsistent and often subjective. In practice, many issues were marked as “High,” reducing the signal value of priority itself.
This led to:
Time spent debating prioritization instead of acting on it
Critical user-facing issues occasionally deprioritized
Flat, unranked backlogs at the start of triage cycles
Two key dimensions were already present:
Severity (technical classification)
Reproduction rate
However, the player impact dimension was missing.
Issues with similar severity could have vastly different real-world impact. For example:
A rare crash in a low-traffic area
A minor but highly visible issue affecting most players
The system did not surface this distinction.
Introduced a lightweight scoring model to complement existing prioritization rather than replace it.
Added a third dimension:
User Impact — a reporter-assessed measure of how disruptive and widespread the issue is for players
Combined into a single score: Bug Score = ((wS × Severity) × (wR × Repro Rate) × (wI × User Impact)) / N
The multiplicative model ensured:
Low score in any dimension reduces overall priority
Prevents inflation of edge cases
Allows tuning via weights depending on project phase
Implemented automated scoring via Jira ScriptRunner (real-time updates on issue changes)
Exposed Bug Score as a sortable field in triage views and dashboards
Introduced simple scoring guidelines aligned with existing reporting standards
Provided per-assignee dashboards sorted by priority + Bug Score for self-triage
Faster triage start during stabilization (ranked queues instead of flat lists)
Reduced subjective prioritization debates
Improved alignment on what constitutes “high priority”
Enabled engineers to self-manage queues without coordination overhead
Reusable dashboard and query patterns across multiple releases
Small, well-placed systems reduce friction more effectively than process overhauls.
The Bug Score did not replace prioritization decisions, it accelerated them. By making implicit reasoning visible and consistent, it reduced cognitive load, improved alignment, and enabled faster execution under pressure.
Calibration over time, rather than precision at launch, was key to adoption and long-term usefulness.