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- Introduced a comprehensive template for generating epics and stories in Phase 5, including an index and individual epic files. - Created a product brief template for Phase 2 to summarize product vision, goals, and target users. - Developed a requirements PRD template for Phase 3, outlining functional and non-functional requirements, along with traceability matrices. feat: Implement tech debt roles for assessment, execution, planning, scanning, validation, and analysis - Added roles for tech debt assessment, executor, planner, scanner, validator, and analyst, each with defined phases and processes for managing technical debt. - Each role includes structured input requirements, processing strategies, and output formats to ensure consistency and clarity in tech debt management.
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3.2 KiB
role, prefix, inner_loop, message_types
| role | prefix | inner_loop | message_types | ||||||
|---|---|---|---|---|---|---|---|---|---|
| analyst | QAANA | false |
|
Quality Analyst
Analyze defect patterns, coverage gaps, test effectiveness, and generate comprehensive quality reports. Maintain defect pattern database and provide quality scoring.
Phase 2: Context Loading
| Input | Source | Required |
|---|---|---|
| Task description | From task subject/description | Yes |
| Session path | Extracted from task description | Yes |
| .msg/meta.json | /wisdom/.msg/meta.json | Yes |
| Discovered issues | meta.json -> discovered_issues | No |
| Test strategy | meta.json -> test_strategy | No |
| Generated tests | meta.json -> generated_tests | No |
| Execution results | meta.json -> execution_results | No |
| Historical patterns | meta.json -> defect_patterns | No |
- Extract session path from task description
- Read .msg/meta.json for all accumulated QA data
- Read coverage data from
coverage/coverage-summary.jsonif available - Read layer execution results from
<session>/results/run-*.json - Select analysis mode:
| Data Points | Mode |
|---|---|
| <= 5 issues + results | Direct inline analysis |
| > 5 | CLI-assisted deep analysis via gemini |
Phase 3: Multi-Dimensional Analysis
Five analysis dimensions:
- Defect Pattern Analysis: Group issues by type/perspective, identify patterns with >= 2 occurrences, record type/count/files/description
- Coverage Gap Analysis: Compare actual coverage vs layer targets, identify per-file gaps (< 50% coverage), severity: critical (< 20%) / high (< 50%)
- Test Effectiveness: Per layer -- files generated, pass rate, iterations needed, coverage achieved. Effective = pass_rate >= 95% AND iterations <= 2
- Quality Trend: Compare against coverage_history. Trend: improving (delta > 5%), declining (delta < -5%), stable
- Quality Score (0-100 starting from 100):
| Factor | Impact |
|---|---|
| Security issues | -10 per issue |
| Bug issues | -5 per issue |
| Coverage gap | -0.5 per gap percentage |
| Test failures | -(100 - pass_rate) * 0.3 per layer |
| Effective test layers | +5 per layer |
| Improving trend | +3 |
For CLI-assisted mode:
PURPOSE: Deep quality analysis on QA results to identify defect patterns and improvement opportunities
TASK: Classify defects by root cause, identify high-density files, analyze coverage gaps vs risk, generate recommendations
MODE: analysis
Phase 4: Report Generation & Output
- Generate quality report markdown with: score, defect patterns, coverage analysis, test effectiveness, quality trend, recommendations
- Write report to
<session>/analysis/quality-report.md - Update
<session>/wisdom/.msg/meta.json:defect_patterns: identified patterns arrayquality_score: calculated scorecoverage_history: append new data point (date, coverage, quality_score, issues)
Score-based recommendations:
| Score | Recommendation |
|---|---|
| >= 80 | Quality is GOOD. Maintain current testing practices. |
| 60-79 | Quality needs IMPROVEMENT. Focus on coverage gaps and recurring patterns. |
| < 60 | Quality is CONCERNING. Recommend comprehensive review and testing effort. |