# Epic 15: Comprehensive Testing & Quality

**Goal**: Expand testing to include performance testing, chaos engineering, security testing, and continuous quality monitoring beyond MVP coverage.

**Value**: Ensures production reliability under extreme conditions, identifies security vulnerabilities proactively, and maintains quality as codebase grows.

**Priority**: Medium (Quality improvement)

---

## Story 15.1: Performance & Load Testing Suite

As a performance engineer,
I want automated performance tests that simulate high load scenarios,
So that I can validate performance under stress and identify bottlenecks before production.

**Acceptance Criteria:**

**Given** performance test suite
**When** load tests execute
**Then** tests simulate: 1000 concurrent operations, 10,000 messages/minute publish rate, 100 concurrent consumers

**And** performance metrics measured: throughput (ops/sec), latency percentiles (p50/p95/p99/p999), error rate, resource usage (CPU/memory)

**And** load test scenarios: normal load (baseline), peak load (2x normal), stress test (10x normal, until failure)

**And** soak test: sustained load for 1 hour to detect memory leaks, connection leaks

**And** performance regression detection: compare against baseline, fail if degradation >10%

**And** test reports generated: ./reports/performance-{timestamp}.html with graphs, tables, summary

**And** load tests run in CI/CD: scheduled nightly, on release branches

**Prerequisites:** Epic 6 complete (testing framework), Story 12.1 (metrics)

**Technical Notes:**
- Use locust or pytest-benchmark for load testing
- Locust scenario: simulate users executing operations (list queues, publish messages, etc.)
- Resource monitoring: psutil for CPU/memory, track over test duration
- Baseline: establish performance baseline for comparison (save to ./baselines/performance.json)
- Regression detection: compare current run to baseline, fail if p95 latency >10% higher
- Reports: use locust HTML reports or custom reporting with matplotlib
- CI/CD: GitHub Actions scheduled workflow, publish reports as artifacts

---

## Story 15.2: Chaos Engineering Tests

As a reliability engineer,
I want chaos engineering tests that inject failures,
So that I can validate system resilience and recovery behavior.

**Acceptance Criteria:**

**Given** chaos testing framework
**When** failures are injected
**Then** tests validate recovery from: RabbitMQ server crash (reconnection), network partition (retry logic), slow RabbitMQ responses (timeout handling), memory pressure (graceful degradation)

**And** chaos scenarios: kill RabbitMQ container mid-operation, introduce network latency (100ms, 500ms, 1000ms), simulate packet loss (5%, 10%, 20%), fill disk space (log rotation behavior)

**And** recovery validation: operations resume after failure, no data loss, no memory leaks, connection reestablished, operations complete successfully

**And** chaos tests use: toxiproxy for network chaos, Docker testcontainers for RabbitMQ control

**And** chaos test reports: ./reports/chaos-{timestamp}.md with scenarios, results, recovery times

**And** chaos tests run: manually triggered, scheduled weekly in CI/CD

**Prerequisites:** Epic 2 complete (connection management), Story 6.4 (integration tests)

**Technical Notes:**
- Use toxiproxy: proxy between MCP server and RabbitMQ, inject latency/packet loss
- Docker control: docker stop rabbitmq (crash), docker pause rabbitmq (freeze)
- Scenarios: happy path baseline → inject failure → verify recovery → measure recovery time
- Recovery metrics: time to detect failure, time to reconnect, operations during downtime (should queue or fail gracefully)
- Chaos framework: define scenarios in YAML, execute with pytest
- CI/CD: separate workflow for chaos tests (weekly schedule)

---

## Story 15.3: Security Testing & Vulnerability Scanning

As a security engineer,
I want automated security testing and vulnerability scanning,
So that I can identify and fix security issues before they reach production.

**Acceptance Criteria:**

**Given** security testing suite
**When** security tests run
**Then** tests validate: no credentials in logs (automated detection), SQL injection prevention (if using SQL), XSS prevention (if web UI added), authentication bypass attempts (fail), authorization bypass attempts (fail)

**And** vulnerability scanning includes: dependency scanning (safety, pip-audit), SAST (bandit, semgrep), secrets scanning (detect-secrets, truffleHog), license compliance

**And** security tests run: on every commit (fast checks), nightly (full scan)

**And** vulnerability reports generated: ./reports/security-{timestamp}.json with findings, severity, remediation

**And** CI/CD blocks merge if: critical vulnerabilities found, credentials detected in code, banned dependencies used

**And** security dashboard: track vulnerability count over time, time to remediation

**Prerequisites:** Story 7.3 (sensitive data sanitization), Story 13.2 (RBAC)

**Technical Notes:**
- Use bandit for SAST: bandit -r src/ -f json -o security-report.json
- Dependency scanning: pip-audit (scans for known vulnerabilities in dependencies)
- Secrets scanning: detect-secrets scan --baseline .secrets.baseline
- RBAC testing: unit tests for permission checks, attempt unauthorized operations
- Credential detection tests: log sample sensitive strings, verify redaction
- CI/CD: GitHub Advanced Security (Dependabot, code scanning), or custom workflows
- Remediation: create issues for findings, track in security dashboard

---
