Metric 01
Defect Leakage Rate
2.4%
↓ 1.1% vs last sprint
Target: < 3%
✓ Target Met
Metric 02
Automation Coverage
78%
↑ 5% vs last sprint
Target: > 80%
⚠ 2% below target
Metric 03
Test Pass Percentage
96.8%
↑ 2.3% vs last sprint
Target: > 95%
✓ Target Met
Metric 04
Regression Exec. Time
1h 22m
↓ 18 min saved
Was: 10 days manual
✓ Fully Automated
Metric 05
Defect Reopen Ratio
3.8%
↓ 1.2% vs last sprint
Target: < 5%
✓ Target Met
Direct Release Quality Indicator
Defect Leakage
Measures how many defects escaped to Production or UAT after QA validation. The most visible metric in leadership and client reviews — directly reflects testing effectiveness and release confidence.
Current Sprint
2.4%
Sprint 24
Prod Defects
3
this sprint
Total Defects
124
raised
Status
✅
Target < 3%
6-Sprint Trend · Defect Leakage %
Sprint-by-Sprint Data
| Sprint | Prod Defects | Total Defects | Leakage % |
|---|---|---|---|
| Sprint 19 | 8 | 123 | 6.5% |
| Sprint 20 | 9 | 125 | 7.2% |
| Sprint 21 | 7 | 121 | 5.8% |
| Sprint 22 | 6 | 130 | 4.6% |
| Sprint 23 | 5 | 143 | 3.5% |
| Sprint 24 ◀ | 3 | 124 | 2.4% |
Formula
Calculation
Defect Leakage
= ( Production Defects /
Total Defects Raised ) × 100
= ( 3 / 124 ) × 100 = 2.4%
= ( Production Defects /
Total Defects Raised ) × 100
= ( 3 / 124 ) × 100 = 2.4%
Leadership Impact · Why Leaders Care
- Lower leakage = better customer experience and fewer production incidents
- Directly reflects the effectiveness of the QA testing strategy
- Highly visible in client-facing SLA reports and leadership scorecards
- Drives release confidence — leadership approves Go/No-Go based on this
- Reduction from 7.2% → 2.4% over 6 sprints = measurable QA improvement story
By Component · Sprint 24
Automation Maturity Indicator
Automation Coverage
Measures what percentage of the total test cases are covered by automation. Indicates automation maturity, directly reduces regression effort, and accelerates CI/CD delivery cycles.
Coverage
78%
Sprint 24
Automated TCs
973
of 1,248 total
Target Gap
−2%
needs 27 more TCs
Status
⚠️
Target > 80%
Coverage by Module
6-Sprint Growth
| Sprint | Automated | Total TCs | Coverage |
|---|---|---|---|
| Sprint 19 | 620 | 1,180 | 52% |
| Sprint 20 | 680 | 1,195 | 57% |
| Sprint 21 | 740 | 1,210 | 61% |
| Sprint 22 | 810 | 1,220 | 66% |
| Sprint 23 | 880 | 1,235 | 71% |
| Sprint 24 ◀ | 973 | 1,248 | 78% |
Formula
Calculation
Automation Coverage
= ( Automated Test Cases /
Total Test Cases ) × 100
= ( 973 / 1,248 ) × 100 = 78%
= ( Automated Test Cases /
Total Test Cases ) × 100
= ( 973 / 1,248 ) × 100 = 78%
Leadership Impact
- Faster CI/CD pipelines — automated suites run in 1h 22m vs 10 manual days
- Lower manual testing effort — releases 3× faster with same team size
- Improved regression stability — fewer human errors in repetitive test execution
- Coverage growth 52% → 78% over 6 sprints demonstrates team maturity
- Gap analysis: Legacy APIs at 38% — priority target for next sprint
Automation Tool Breakdown
Build Quality & Release Readiness
Test Pass Percentage
Measures execution success rate across all test cases run in a sprint. The primary indicator of build quality, sprint health, and release readiness — tracked at every sprint review and release gate.
Pass Rate
96.8%
Sprint 24
Passed
1,208
test cases
Failed
26
test cases
Skipped
14
deferred
Suite-Level Pass Rate · Sprint 24
6-Sprint Pass Rate History
| Sprint | Passed | Failed | Pass % |
|---|---|---|---|
| Sprint 19 | 1,050 | 130 | 89.0% |
| Sprint 20 | 1,080 | 115 | 90.4% |
| Sprint 21 | 1,110 | 95 | 92.1% |
| Sprint 22 | 1,140 | 68 | 94.4% |
| Sprint 23 | 1,175 | 50 | 95.9% |
| Sprint 24 ◀ | 1,208 | 26 | 96.8% |
Formula
Calculation
Pass Percentage
= ( Passed Test Cases /
Executed Test Cases ) × 100
= ( 1,208 / 1,248 ) × 100 = 96.8%
= ( Passed Test Cases /
Executed Test Cases ) × 100
= ( 1,208 / 1,248 ) × 100 = 96.8%
Leadership Impact
- 96.8% pass rate exceeds 95% target — release candidate approved for Sprint 24
- 89% → 96.8% trend over 6 sprints indicates improving development quality
- Stable pass rates reduce rework cycles and sprint spillover
- UAT at 91% signals a minor business alignment gap — follow-up action item
- Higher pass % = fewer emergency hotfixes and post-release patches
Failure Root Cause Analysis · Sprint 24
Automation Efficiency · CI/CD Velocity
Regression Execution Time
Measures the total time required to complete regression testing. Critical for Agile and CI/CD delivery — demonstrates automation ROI and directly impacts release timelines and deployment frequency.
Current Time
1h 22m
Sprint 24
Manual Baseline
10d
before automation
Time Saved
~98%
reduction
Runs/Day
12×
on CI pipeline
Regression Time Reduction Journey
Before Automation
10 Days
100% manual
6 Months Later
12 Hours
partial automation
Sprint 24 Now
1h 22m
78% automated
Sprint-by-Sprint Execution Time
| Sprint | Time (hh:mm) | TCs Run | Automation % |
|---|---|---|---|
| Sprint 19 | 6:30 | 1,180 | 52% |
| Sprint 20 | 5:45 | 1,195 | 57% |
| Sprint 21 | 4:50 | 1,210 | 61% |
| Sprint 22 | 3:30 | 1,220 | 66% |
| Sprint 23 | 2:15 | 1,235 | 71% |
| Sprint 24 ◀ | 1:22 | 1,248 | 78% |
Why It Matters — No Formula, Just Impact
Key Insight
10 days manual → 1h 22m automated
ROI = ~98.6% time reduction
CI runs/day = 12 pipeline executions
Team effort saved = ~9 QA-days/sprint
ROI = ~98.6% time reduction
CI runs/day = 12 pipeline executions
Team effort saved = ~9 QA-days/sprint
Leadership Impact
- Release cycle compressed from 2-week manual regression to sub-2-hour runs
- CI pipeline executes 12 full regression runs daily — catches issues immediately
- Frees up ~9 QA-days per sprint for exploratory and risk-based testing
- Enables same-day hotfix validation — critical for BFSI production incidents
- Demonstrates direct ROI of automation investment to finance and leadership
Suite Execution Time Breakdown
Fix Quality & Dev-QA Collaboration
Defect Reopen Ratio
Measures what percentage of defects were reopened after being marked as fixed. Indicates the quality of fixes, communication effectiveness between Dev and QA teams, and overall retesting overhead.
Reopen Ratio
3.8%
Sprint 24
Reopened
7
defects
Total Closed
184
this sprint
Status
✅
Target < 5%
6-Sprint Reopen Ratio Trend
Sprint Data
| Sprint | Reopened | Total Closed | Ratio % |
|---|---|---|---|
| Sprint 19 | 15 | 183 | 8.2% |
| Sprint 20 | 13 | 173 | 7.5% |
| Sprint 21 | 12 | 176 | 6.8% |
| Sprint 22 | 10 | 182 | 5.5% |
| Sprint 23 | 9 | 180 | 5.0% |
| Sprint 24 ◀ | 7 | 184 | 3.8% |
Formula
Calculation
Defect Reopen Ratio
= ( Reopened Defects /
Total Closed Defects ) × 100
= ( 7 / 184 ) × 100 = 3.8%
= ( Reopened Defects /
Total Closed Defects ) × 100
= ( 7 / 184 ) × 100 = 3.8%
Leadership Impact
- 3.8% well below 5% target — reflects strong Dev-QA collaboration this sprint
- Reduction from 8.2% → 3.8% shows improved fix quality and code review processes
- Lower reopen ratio = less retesting overhead, faster sprint throughput
- High reopen ratios (>5%) are a key flag for leadership on team process gaps
- Strong indicator used in QA maturity assessments and vendor evaluations
Reopen Reasons · Sprint 24