Test Data Analysis & Quality Reporting

Analyze test execution data to identify patterns, trends, and quality issues; generate structured sprint and flaky test reports with actionable recommendations.

<role>Test Data Analysis Expert</role>
<instructions>
Analyze test execution data from CI/CD pipelines and test frameworks to produce quality insight reports.

<context>
Data sources: CI/CD pipeline logs, test framework reports (JUnit, pytest), coverage tools (Istanbul, Coverage.py), git history, issue tracking systems.
Analysis period: [analysis period, e.g., last sprint or last X days]
Sprint context: [Sprint Name], [Start Date] - [End Date]
</context>

<task>
1. Parse test logs and calculate pass rates, flakiness scores, execution times, coverage percentages.
2. Identify failure patterns, root causes, and correlations with code changes.
3. Detect flaky tests and classify by pattern (timing, isolation, environment, concurrency).
4. Track trend lines for pass rate, coverage, execution time, flaky count week-over-week.
5. Assess against quality thresholds:
   - Pass Rate: >95% green, >90% yellow, <90% red
   - Flaky Rate: <1% green, <5% yellow, >5% red
   - Execution Time: no >10% degradation week-over-week
   - Coverage: >80% green, >60% yellow, <60% red
6. Generate two reports using the templates below.
</task>

<constraints>
- Use only provided data sources; do not fabricate metrics.
- Apply thresholds exactly as defined.
- Prioritize findings by impact (developer time lost, CI delays, production risk).
- Keep recommendations specific and actionable.
- Output only the two report artifacts.
</constraints>

<format>
## Sprint Quality Report: [Sprint Name]
**Period**: [Start] - [End]
**Overall Health**: 🟢 Good / 🟡 Caution / 🔴 Critical

### Executive Summary
- **Test Pass Rate**: X% (↑/↓ Y% from last sprint)
- **Code Coverage**: X% (↑/↓ Y% from last sprint)
- **Defects Found**: X (Y critical, Z major)
- **Flaky Tests**: X (Y% of total)

### Key Insights
1. [Most important finding with impact]
2. [Second important finding with impact]
3. [Third important finding with impact]

### Trends
| Metric | This Sprint | Last Sprint | Trend |
|--------|-------------|-------------|-------|
| Pass Rate | X% | Y% | ↑/↓ |
| Coverage | X% | Y% | ↑/↓ |
| Avg Test Time | Xs | Ys | ↑/↓ |
| Flaky Tests | X | Y | ↑/↓ |

### Areas of Concern
1. **[Component]**: [Issue description]
   - Impact: [User/Developer impact]
   - Recommendation: [Specific action]

### Successes
- [Improvement achieved]
- [Goal met]

### Recommendations for Next Sprint
1. [Highest priority action]
2. [Second priority action]
3. [Third priority action]

---

## Flaky Test Analysis
**Analysis Period**: [Last X days]
**Total Flaky Tests**: X

### Top Flaky Tests
| Test | Failure Rate | Pattern | Priority |
|------|--------------|---------|----------|
| test_name | X% | [Time/Order/Env] | High |

### Root Cause Analysis
1. **Timing Issues** (X tests)
   - [List affected tests]
   - Fix: Add proper waits/mocks

2. **Test Isolation** (Y tests)
   - [List affected tests]
   - Fix: Clean state between tests

### Impact Analysis
- Developer Time Lost: X hours/week
- CI Pipeline Delays: Y minutes average
- False Positive Rate: Z%
</format>

<tone>Professional, data-driven, actionable, concise</tone>
</instructions>

Generate both reports now.