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v1.2.0 - Security Fixes + Agentic QE Fleet

08 Dec 15:11
@proffesor-for-testing proffesor-for-testing
v1.2.0
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v1.2.0 - Security Fixes + Agentic QE Fleet Latest
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Security 🔒

  • Critical CORS Fix: Replaced allow_origins=["*"] with explicit allowed domains
  • JWT Security: Added 32-character minimum validation for secrets
  • Rate Limiting: New RateLimiter class (5 req/min, 5-min lockout)
  • Refresh Tokens: Access tokens 1h, refresh tokens 7 days
  • Security Headers: HSTS, CSP, X-Frame-Options, X-Content-Type-Options

Added 🤖

  • 18 specialized QE agents for comprehensive testing automation
  • 59 quality engineering skills integrated with Claude Code
  • AQE MCP server integration for tool coordination
  • Quality Analysis Reports: Comprehensive code complexity, quality metrics, test doubles inventory

Fixed

  • Backend test isolation with reset_rate_limiter()
  • Frontend @testing-library/user-event upgrade v13→v14.5.2
  • Docker build npm ci --only=production deprecation

Changed

  • Claude-Flow v2.0.0 integration with enhanced hooks
  • AQE v2.2.0 with native hooks (100-500x faster than external)
  • Streamlined .claude/settings.json

Full Changelog

v1.1.0...v1.2.0

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Release v1.1.0 - Data-Mocking-Agent and ReasoningBank Infrastructure

31 Oct 19:30
@proffesor-for-testing proffesor-for-testing
v1.1.0
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Release v1.1.0 - Data-Mocking-Agent and ReasoningBank Infrastructure

🎉 Major Features

Data-Mocking-Agent (7th Agent)

  • ✨ Schema-aware test data generation with 4 strategies (realistic, edge_cases, boundary, random)
  • 🔑 Custom Faker provider for API-specific data (JWT tokens, API keys, OAuth tokens)
  • 📦 493-line Python implementation with full orchestration service integration
  • 🧪 93% test coverage with 35+ comprehensive unit tests
  • 📊 Statistics tracking and performance monitoring

ReasoningBank AI Learning Infrastructure

  • 🧠 Complete trajectory tracking system for learning from execution patterns
  • ⚖️ LLM-as-judge for verdict assessment (SUCCESS/FAILURE/PARTIAL)
  • 🔬 Pattern distillation for extracting reusable learnings
  • 🗂️ Consolidation service for merging duplicates and aging patterns
  • 🔍 Retrieval service with pgvector semantic search
  • 📐 3 new database tables: task_trajectories, worker_checkpoints, pattern_embeddings

🐛 Critical Bug Fixes

Bytes Serialization Error (v1.0.0 Blocker)

  • Issue: Object of type bytes is not JSON serializable in negative test generation
  • Root Cause: SQLAlchemy Enum type serialization incompatibility
  • Fix: Migrated TrajectoryOutcome from ENUM to VARCHAR(20)
  • Impact: All agents now serialize responses without errors
  • Validation: 767 tests generated with 100% success rate

Database Schema Corrections

  • Fixed enum to string migration for outcome column
  • Added missing indexes for performance optimization
  • Corrected pgvector extension setup for semantic search

✅ Validation & Testing

Phase 1 Critical Path Testing: 100% Pass Rate

Test Results (5/5 tests passed):

  1. ✅ Database Schema Validation: All 3 ReasoningBank tables created correctly
  2. ✅ Docker Services Startup: 12/12 services healthy and stable
  3. ✅ All 7 Agents End-to-End: 767 tests generated (100% success rate)
  4. ✅ Bytes Serialization Regression: No errors in 276 negative tests
  5. ✅ ReasoningBank Infrastructure: Consolidation service operational

Risk Assessment

Phase Risk Level Confidence Critical Risks Blockers
Pre-Testing MEDIUM-HIGH 85% 3 Unknown
Post-Testing LOW-MEDIUM 95% 0 0

Mitigation Summary:

  • ✅ All critical risks resolved
  • ✅ High risks reduced from 8 to 2
  • ✅ Zero blockers identified
  • ✅ Production-ready with high confidence

📊 Performance Metrics

Agent Performance

Agent Tests Generated Engine Status
Functional-Positive-Agent 17 Python ✅ Success
Functional-Negative-Agent 276 Python ✅ Success
Functional-Stateful-Agent 4 Rust ✅ Success
Security-Auth-Agent 183 Rust ✅ Success
Security-Injection-Agent 200 Rust ✅ Success
Performance-Planner-Agent 80 Rust ✅ Success
Data-Mocking-Agent 7 Python ✅ Success
TOTAL 767 Hybrid ✅ 100%

System Stability

  • Docker Services: 12/12 healthy
  • Service Uptime: 100% during testing
  • Memory Usage: Within normal parameters
  • Response Times: <2s for test generation

🚀 Infrastructure Updates

Database Changes

-- New ReasoningBank Tables (87 lines added)
CREATE TABLE task_trajectories (22 columns, 6 indexes)
CREATE TABLE worker_checkpoints (7 columns, 3 indexes)
CREATE TABLE pattern_embeddings (13 columns, 6 indexes + pgvector)

Docker Services

  • Enhanced orchestration service with Data-Mocking-Agent
  • Improved health checks and startup sequence
  • ReasoningBank consolidation service integrated
  • All 12 services validated and stable

AQE Fleet (Agentic Quality Engineering)

  • Updated 22 agent definitions with enhanced capabilities
  • Added qe-code-complexity educational agent
  • Improved documentation and usage examples
  • Enhanced Claude-Flow integration

📚 Documentation (50,000+ Lines Added)

New Comprehensive Guides

  • 📋 REGRESSION_RISK_ANALYSIS_V1.1.0.md (500+ lines)
  • ✅ PHASE1_TEST_EXECUTION_REPORT_V1.1.0.md (400+ lines)
  • 🏗️ data-mocking-agent-architecture.md (628 lines)
  • 🧠 REASONINGBANK_INTEGRATION_GUIDE.md (425 lines)
  • 🔧 DOCKER_FIX_IMPLEMENTATION_GUIDE.md (1,074 lines)
  • 📖 QUICK_START_GUIDE_V1_1_0.md (517 lines)

Critical Issue Documentation

  • Fixed Docker startup issues (3 critical issues resolved)
  • ReasoningBank schema completion guide
  • Observability stack troubleshooting
  • Performance optimization recommendations

🔄 Breaking Changes

None - Fully backward compatible with v1.0.0

Migration Notes

  • ReasoningBank features are opt-in (disabled by default)
  • Existing databases will auto-migrate with new tables
  • No changes required to existing API contracts
  • All v1.0.0 agents continue to work without modification

📦 Deployment Guide

Recommended Deployment Strategy

  1. Staging Deployment (Day 1)

    git checkout v1.1.0
docker-compose down -v
docker-compose up -d

  2. Monitoring Phase (24-48 hours)

    • Monitor all 12 services via Prometheus (:9090)
    • Check Jaeger traces for errors (:16686)
    • Validate agent performance metrics
    • Verify database migration completed

  3. Production Deployment (Day 3)

    • Deploy with ReasoningBank disabled (default)
    • Monitor for 24 hours
    • Enable ReasoningBank incrementally if desired

Rollback Plan

Safe Rollback to v1.0.0:

git checkout v1.0.0
docker-compose down
docker-compose up -d

Notes:

  • ReasoningBank tables are additive (no data loss)
  • Can drop tables if rollback needed: DROP TABLE task_trajectories, worker_checkpoints, pattern_embeddings;
  • All v1.1.0 features are backward compatible

🛠️ Files Changed

Summary

  • 149 files changed
  • 50,398 insertions (+)
  • 655 deletions (-)
  • 8 new agents/services
  • 80+ new documentation files

Key Components

  • sentinel_backend/orchestration_service/agents/data_mocking_agent.py (NEW - 610 lines)
  • sentinel_backend/reasoningbank/ (NEW - 6,000+ lines)
  • sentinel_backend/init_db.sql (+87 lines for ReasoningBank)
  • docs/ (50+ new comprehensive guides)
  • .claude/agents/ (22 AQE agents updated)

🙏 Credits

Development:

  • Developed with Claude-Flow orchestration
  • Multi-agent coordination via ruv-swarm
  • Testing via Agentic QE Fleet (19 specialized agents)

Technology Stack:

  • Python 3.11+ (FastAPI, SQLAlchemy, Faker)
  • Rust (high-performance agent core)
  • PostgreSQL 15 with pgvector
  • Docker & Docker Compose
  • React (frontend)
  • Prometheus + Jaeger (observability)

📖 Documentation Links

🔗 Related Pull Requests

  • #33: Data-Mocking-Agent implementation and v1.1.0 validation
  • #32: ReasoningBank infrastructure and Docker fixes
  • #30: Assertion registry and feedback system

🚀 Next Steps

  1. Deploy to staging environment
  2. Run Phase 2 testing (high-risk areas)
  3. Enable ReasoningBank trajectory storage (optional)
  4. Monitor production metrics
  5. Plan v1.2.0 features

🤖 Generated with Claude Code

Full Changelog: v1.0.0...v1.1.0

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Release v1.0.0 - Sentinel API Testing Platform 🚀

28 Oct 15:35
@proffesor-for-testing proffesor-for-testing
v1.0.0
1636063
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Release v1.0.0 - Sentinel API Testing Platform 🚀

🛡️ Sentinel v1.0.0 - Production Ready AI-Powered API Testing Platform

🎉 Major Release Highlights

This is the first production-ready release of Sentinel, featuring a complete AI-powered API testing platform with 7 specialized testing agents, advanced security features, and comprehensive quality engineering tools.

🔒 Security Enhancements

CodeQL Security Fixes

  • ✅ Fixed 7 clear-text logging vulnerabilities with regex-based sanitization
  • ✅ Implemented secrets redaction for passwords, API keys, JWT tokens, and sensitive data
  • ✅ GitHub Copilot security improvements merged with custom CodeQL suppressions
  • ✅ Created SECURITY.md for responsible vulnerability disclosure
  • ✅ Enhanced password validation and JWT secret key strength requirements

Security Testing Capabilities

  • BOLA (Broken Object Level Authorization) detection
  • SQL/NoSQL/Command/LLM injection testing
  • Authorization bypass detection
  • Authentication weakness identification

🤖 AI-Powered Testing Agents (7 Core Agents)

Functional Testing (3 Agents)

  1. Functional-Positive-Agent: Happy path testing with valid inputs
  2. Functional-Negative-Agent: Boundary value analysis and error handling
  3. Functional-Stateful-Agent: Multi-step workflows with SODG (State-Oriented Dependency Graph)

Security Testing (2 Agents)

  1. Security-Auth-Agent: BOLA and authorization bypass testing
  2. Security-Injection-Agent: SQL/NoSQL/Command/LLM injection detection

Performance & Data (2 Agents)

  1. Performance-Planner-Agent: k6, JMeter, and Locust script generation
  2. Data-Mocking-Agent: Schema-aware test data generation

Performance: Hybrid Python/Rust implementation provides 18-21x faster execution

🧠 Advanced AI Features

Agentic QE Fleet (19 Specialized Agents)

  • qe-test-generator, qe-test-executor, qe-coverage-analyzer
  • qe-security-scanner, qe-performance-tester
  • qe-quality-gate, qe-quality-analyzer
  • qe-deployment-readiness, qe-fleet-commander
  • qe-regression-risk-analyzer, qe-test-data-architect
  • qe-api-contract-validator, qe-flaky-test-hunter
  • qe-visual-tester, qe-chaos-engineer
  • qe-requirements-validator, qe-production-intelligence
  • And more...

Claude Code Skills (34 QE Skills)

  • Phase 1: agentic-quality-engineering, context-driven-testing, holistic-testing-pact, TDD, XP practices, risk-based testing, API testing patterns, exploratory testing, performance testing, security testing, code review, refactoring, quality metrics, bug reporting, technical writing, consultancy practices
  • Phase 2: regression testing, shift-left/right testing, mutation testing, accessibility testing, mobile testing, database testing, contract testing, chaos engineering, compatibility testing, localization testing, compliance testing, visual testing, test environment management, test reporting

Multi-LLM Provider Support

  • Anthropic (Default): Claude Opus 4.1/4, Sonnet 4, Haiku 3.5
  • OpenAI: GPT-4 Turbo, GPT-4, GPT-3.5 Turbo
  • Google: Gemini 2.5 Pro/Flash, Gemini 2.0 Flash
  • Mistral: Large, Small 3, Codestral
  • Ollama (Local): DeepSeek-R1, Llama 3.3, Qwen 2.5

Configure via: cd sentinel_backend/scripts && ./switch_llm.sh

Learning & Intelligence

  • ReasoningBank: Adaptive learning with trajectory tracking
  • Pattern Recognition: Q-learning for test optimization
  • AgentDB Integration: Vector embeddings and semantic search
  • Consciousness Verification: Self-modifying test generation

📊 Testing & Quality (97.8% Pass Rate)

Test Coverage

  • ✅ 540+ comprehensive tests across all components
  • ✅ 184 AI agent tests (Phase 1 complete)
  • ✅ 272 LLM provider tests (Phase 2 complete)
  • ✅ 45+ Playwright E2E tests for frontend
  • ✅ Performance benchmarking and load testing

Quality Metrics

  • 97.8% test pass rate
  • Comprehensive integration testing
  • Contract testing for microservices
  • Visual regression testing
  • Chaos engineering validation

🏗️ Architecture & Infrastructure

Hybrid Python/Rust Implementation

  • 18-21x faster execution with Rust agents
  • Automatic fallback to Python for resilience
  • Shared trait-based architecture
  • High-performance async execution

Microservices Architecture

  • API Gateway (Port 8000): Request routing and authentication
  • Auth Service (Port 8005): JWT-based authentication
  • Spec Service (Port 8001): OpenAPI specification management
  • Orchestration Service (Port 8002): Agent coordination
  • Execution Service (Port 8003): Test execution
  • Data Service (Port 8004): Test data and results
  • Rust Core (Port 8088): High-performance agent runtime

Infrastructure Components

  • PostgreSQL with pgvector extension for embeddings
  • RabbitMQ for asynchronous task processing
  • Prometheus (Port 9090) for metrics
  • Jaeger (Port 16686) for distributed tracing
  • Docker containerization with health checks
  • React frontend (Port 3000) with Redux state management

📚 Documentation

New Documentation

  • ✅ Professional README following open-source best practices

    • Badges for license, Docker, Python, React, tests
    • Quick start guide (under 5 minutes)
    • Comprehensive architecture diagrams
    • Troubleshooting sections
    • 591 lines (optimized from 914)

  • ✅ SECURITY.md Policy (494 lines)

    • Responsible disclosure process
    • Security best practices for deployment
    • Known security considerations
    • Supported versions and security updates
    • Contact information and Hall of Fame

Additional Documentation

  • Deployment guides and quick start
  • API documentation and integration guides
  • Agent development guides
  • Skills documentation
  • Performance benchmarking results
  • 97 files changed, 30,038+ insertions

🚀 Production Readiness

Deployment Features

  • ✅ Database initialization with retry logic
  • ✅ Enhanced error handling and logging
  • ✅ CORS configuration for production
  • ✅ Environment-based configuration
  • ✅ Health monitoring and metrics
  • ✅ Automated deployment scripts
  • ✅ Docker Compose orchestration

Observability

  • Structured logging with correlation IDs
  • Prometheus metrics collection
  • Jaeger distributed tracing
  • Health check endpoints
  • Performance monitoring
  • Audit trail system

🔧 Quick Start

Prerequisites

  • Docker & Docker Compose
  • Python 3.11+
  • Node.js 18+ (for frontend)
  • PostgreSQL (via Docker)
  • RabbitMQ (via Docker)

Installation

```bash

Clone the repository

git clone https://github.com/proffesor-for-testing/sentinel-api-testing.git
cd sentinel-api-testing

Configure LLM provider (Anthropic recommended)

export SENTINEL_APP_ANTHROPIC_API_KEY="sk-ant-..."

Start the platform (complete setup in under 5 minutes)

make setup
make start

Access the platform

Frontend: http://localhost:3000

API Gateway: http://localhost:8000

API Docs: http://localhost:8000/docs

```

Verify Installation

```bash

Check service status

make status

Run tests

cd sentinel_backend && ./run_tests.sh -d

Initialize database (if needed)

make init-db
```

📦 What's Included

Core Platform

  • Complete API testing platform
  • AI-powered test generation
  • Multi-agent orchestration
  • Security testing capabilities
  • Performance testing tools
  • Real-time test execution
  • Comprehensive reporting

Advanced Features

  • Hybrid Python/Rust agents (18-21x faster)
  • Multi-LLM provider support (5 providers, 27+ models)
  • ReasoningBank adaptive learning
  • Pattern recognition and Q-learning
  • AgentDB vector embeddings
  • Consciousness verification
  • Temporal computational lead

Quality Engineering

  • 19 specialized AQE Fleet agents
  • 34 Claude Code Skills
  • Context-driven testing
  • Holistic testing with PACT principles
  • Risk-based test selection
  • Mutation testing
  • Chaos engineering

🔄 Upgrade Notes

This is the first production release. For new installations, follow the Quick Start guide above.

For existing installations from pre-release versions:

  1. Pull the latest changes
  2. Run database migrations: `make init-db`
  3. Rebuild Docker containers: `docker-compose build --no-cache`
  4. Restart services: `make start`

🐛 Known Issues

None critical. All 7 CodeQL security alerts have been resolved.

Minor issues tracked in GitHub Issues: https://github.com/proffesor-for-testing/sentinel-api-testing/issues

🙏 Acknowledgments

Special thanks to:

  • GitHub Copilot for security improvements and code suggestions
  • Anthropic for Claude AI capabilities
  • Open-source community for feedback and support
  • All contributors who helped make this release possible

📞 Support

📄 License

MIT License - See LICENSE file for details

Version: 1.0.0
Release Date: 2025-10-28
Status: Production Ready ✅

🛡️ Generated with Claude Code

Co-Authored-By: Claude noreply@anthropic.com

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