Most defect metrics answer only one question: “How many bugs do we have?”
But mature QA organizations need more than volume—they need to understand risk, accumulated quality problems, release readiness, and how effective testing is after launch.
In this article, we cover four high-impact metrics from the Defect Management domain:
- Weighted Number of Submitted Defects
- Quality Debt Index (QDI)
- Bug Fixing Projection
- Test-Phase Issues vs Production Issues in the First Two Weeks
Together, these metrics help teams make better decisions and build confidence in release quality.
1) Weighted Number of Submitted Defects: Turning Bugs Into a Risk Score
Not all defects are equal. A release with 20 cosmetic defects is not the same as a release with 3 critical failures. That’s why weighted defect count is so useful.
Instead of counting defects equally, you assign weights by severity—for example:
- Critical = 5
- High = 3
- Medium = 2
- Low = 1
📐How to measure:
Weighted Score = Σ (defects per severity × severity weight)
This creates a consolidated value that represents not just volume, but impact.
Why it matters
Two releases might both have “10 defects,” but their risk level can be completely different. Weighted scoring helps leaders assess release readiness and compare stability across teams or versions.
Example KPI zones
- 🟢 Green: <20
- 🟡 Amber: 20–40
- 🔴 Red: >40
Recommended actions
- Green: handle as normal backlog.
- Amber: prioritize high-impact issues, rebalance resources.
- Red: pause new features, run focused triage, consider delaying release.
2) Quality Debt Index (QDI): Measuring the Hidden Cost of Deferring Quality
Quality problems often accumulate silently. Teams defer bugs, skip tests, accept code smells, and postpone refactoring to meet short-term deadlines. Over time, delivery slows down, regressions increase, and stability drops.
The Quality Debt Index makes this visible.
There is no universal formula, but the common approach is a weighted model combining indicators such as:
- Unresolved defects (weighted by severity).
- Code quality signals (complexity, duplication, violations from tools like SonarQube).
- Test gaps / low coverage.
- Recent production defects.
The result is an index score representing accumulated quality risk.
How to interpret
- Low QDI: manageable debt, healthy balance between delivery and quality.
- Growing QDI sprint after sprint: warning sign that speed is being bought with long-term instability.
- High QDI: serious risk to maintainability and release confidence.
KPI example
- 🟢 Green: <30
- 🟡 Amber: 30–60
- 🔴 Red: >60 (often lower in regulated industries)
Recommended actions
- Amber: prioritize refactoring, improve test coverage, stop debt from growing.
- Red: slow/pause feature work, pay down debt aggressively, adjust scope or timeline.
3) Bug Fixing Projection: Forecasting When the Backlog Will Stabilize
Leaders often ask: “Can we be stable by release?”
Bug Fixing Projection answers this with a simple but powerful model.
You need two inputs:
- Current number of open defects.
- Average bug fix rate (e.g., bugs closed per week).
Then:
Bug Fixing Projection = Open defects ÷ bugs fixed per week
Why it matters
This transforms bug backlog discussions from opinion to data. If the projection exceeds the time left before release, you have a clear signal that you must adjust priorities, scope, or capacity.
KPI example (relative to time remaining)
- 🟢 Green: 70–100% of remaining time
- 🟡 Amber: 100–130%
- 🔴 Red: >130%
Recommended actions
- Amber: prioritize critical bugs, add capacity, reduce new defect inflow.
- Red: pause features, re-scope release, or delay timeline.
4) Test-Phase Issues vs Production Issues in the First Two Weeks: Validating Test Effectiveness
This metric compares:
- Defects found during testing phases (SIT/UAT/etc.)
vs - Defects found in production in the first two weeks after release
It answers a crucial question:
Did testing prevent early user-facing issues?
You can report it as:
- Ratio (Test issues : Production issues), or
- Test effectiveness percentage:
Test Effectiveness = Test issues ÷ (Test issues + first-2-week production issues)
How to interpret
- High pre-release capture + low production issues = strong test coverage and readiness
- Many early production issues = missing scenarios, weak data, poor environment parity, or quality gates that are too loose
Example KPI zones
- 🟢 Green: >85% caught pre-release
- 🟡 Amber: 70–84%
- 🔴 Red: <70%
Recommended actions
- Amber: enhance coverage and real-world scenarios.
- Red: expand regression suite, tighten acceptance criteria, do RCA on post-release defects, strengthen release gates.
Final Thoughts
These four metrics move QA reporting beyond “how many bugs” into meaningful decision support:
- Weighted defects quantify risk.
- Quality Debt Index exposes accumulated instability.
- Bug Fixing Projection forecasts stabilization.
- Test vs Production (first two weeks) validates release readiness.
Used together, they help teams balance speed, quality, and risk—and they give leadership an evidence-based view of when to release, when to stabilize, and where to invest.