AI测试自动化平台

git clone <repository-url>
cd mcp_integration

# Create virtual environment
python -m venv venv

# Activate (Windows)
.\\venv\\Scripts\\activate

# Install dependencies
pip install -r requirements.txt

# Run the automated setup script
.\\setup_mcp.bat

# GitHub Copilot Direct SDK & GitHub MCP
# Get from: https://github.com/settings/tokens
# Required scopes: read:user, user:email (copilot access required)
GITHUB_TOKEN=your_github_personal_access_token

# Copilot Configuration
COPILOT_MODE=direct  # Use Direct SDK (no VS Code needed)

python check_health.py

playwright install chromium

cd frontend
npm install
cd ..

python check_health.py

✅ MCP Client: OK
✅ Playwright MCP Recorder: Available: True
✅ Microsoft Docs MCP: OK
✅ GitHub MCP: OK
✅ Copilot Client: Direct SDK - Model: gpt-4

cd app/api
uvicorn main:app --reload --port 8001

cd frontend
npm run dev

python -m app.recorder.run_playwright_recorder_v2 --url "https://example.com" --capture-dom --timeout 60

from app.self_healing_with_mcp import SelfHealingExecutor
from pathlib import Path

# Initialize executor
executor = SelfHealingExecutor(framework_root=Path("./framework_repos/my-framework"))

# Run with automatic self-healing (up to 2 retries)
success, logs, healing_attempts = executor.run_trial_with_real_time_healing(
    script_content=my_test_script,
    test_url="https://app.com/page",
    max_retries=2,
    headed=False
)

if success:
    print(f"✅ Test passed after {len(healing_attempts)} healing attempts")

┌──────────────────────────────────────────────────────┐
│  RECORD → EMBED → STORE → QUERY → RETRIEVE → GENERATE │
└──────────────────────────────────────────────────────┘

1. Record browser actions → .refined.json with metadata
2. Embed steps with ChromaDB → 384-dimensional vectors
3. Store in vector database → Semantic index created
4. Query with natural language → "Create supplier flow"
5. Retrieve relevant steps → Hybrid search + reranking
6. Generate test script → LLM + retrieved context = ✨

# 1. Ingest a flow into vector DB
python -m app.ingest_refined_flow --file "app\\generated_flows\\my_flow.refined.json" --flow-name "Create Supplier"

# 2. Query semantically
python -m app.core.vector_db query "create supplier automation" --top-k 10

# 3. Generate test (RAG runs automatically)
# Via API at http://localhost:8001/api/generate/preview

# 1. Record with trace enabled (default)
python -m app.recorder.run_playwright_recorder_v2 --url "https://example.com" --timeout 60

# 2. Analyze the recording
python -m app.recorder.trace_analyzer recordings/<session_name>

# 3. View trace in Playwright Inspector
playwright show-trace recordings/<session_name>/trace.zip

📊 Summary:
  - Trace events: 45
  - Recorded actions: 42
  - Missing events: 3
  - Coverage: 93.3%

❌ Missing Events:
  1. Type: click → Selector: button[data-testid="submit"]
  2. Type: fill → Selector: input#email
  3. Type: check → Selector: input[type="checkbox"]#terms

# Ingest a recorded flow into vector DB for RAG
python -m app.ingest_refined_flow --file "app\\generated_flows\\my_flow.refined.json" --flow-name "Create Supplier"

# Query flows semantically (embedding-based similarity)
python -m app.core.vector_db query "Create Supplier" --top-k 5

# List all ingested flows
python -m app.core.vector_db list --limit 50

from app.core.mcp_client import get_microsoft_docs_mcp

docs_mcp = get_microsoft_docs_mcp()

# Search documentation
results = docs_mcp.search_docs("Playwright locators")

# Get code samples
samples = docs_mcp.search_code_samples("getByRole", language="typescript")

from app.core.mcp_client import get_github_mcp

github = get_github_mcp()

# Search code
code_results = github.search_code("playwright locators")

# Clone repository
github.clone_repository(repo_url, target_path)

mcp_integration/
├── app/                        # Core application
│   ├── api/                   # FastAPI backend
│   ├── core/                  # Core utilities
│   │   ├── llm_client_direct.py # GitHub Copilot Direct SDK
│   │   ├── mcp_client.py      # MCP server clients
│   │   ├── vector_db.py       # RAG Vector DB (ChromaDB)
│   │   └── browser_utils.py   # Browser utilities
│   ├── generators/            # Test generation with RAG
│   │   └── agentic_script_agent.py  # AI script generation + RAG
│   ├── recorder/              # Recording module
│   │   ├── run_playwright_recorder_v2.py
│   │   └── mcp_integration.py # Recorder MCP integration
│   ├── ingestion/             # RAG ingestion pipeline
│   │   └── ingest_refined_flow.py  # Ingest flows to vector DB
│   └── services/              # Business logic
├── docs/                       # Documentation
│   ├── COPILOT_DIRECT_SDK.md  # Direct SDK guide
│   ├── QUICKSTART_COPILOT_DIRECT.md # 5-min setup
│   ├── MCP_INTEGRATION.md     # MCP integration
│   ├── RUNTIME_SELF_HEALING.md # Self-healing guide
│   └── [30+ other guides]     # Complete documentation
├── scripts/                    # Setup & utility scripts
│   ├── setup_mcp.bat          # MCP setup
│   ├── install_browsers.bat   # Playwright installation
│   ├── quick_start.bat        # One-command startup
│   └── [4 more scripts]       # Other utilities
├── examples/                   # Sample code
│   ├── locators/              # Example locator files
│   └── pages/                 # Example page objects
├── generator/                  # Legacy JS generators & prompts
│   ├── codeGenerator.js       # JavaScript code generator
│   ├── COMPLETE_SYSTEM_PROMPT.md # AI prompts
│   └── [8 more files]         # Prompt templates
├── templates/                  # Code templates
├── frontend/                   # React UI
├── tests/                      # Test suite
├── framework_repos/           # Cloned test frameworks
├── vector_store/              # ChromaDB storage (RAG embeddings)
├── .vscode/
│   └── mcp.json               # MCP server configuration
├── .env.template              # Environment variables template
├── README.md                  # This file
├── requirements.txt           # Python dependencies
└── package.json               # Node.js dependencies (MCP)

# Run all tests
python -m pytest -q

# Run specific test file
python -m pytest app/tests/test_vector_db.py -v

# Run with coverage
python -m pytest --cov=app --cov-report=html

Error: Authentication failed. Please check your GITHUB_TOKEN

Error: npx: command not found

Error: Rate limit exceeded. Please try again later.

ModuleNotFoundError: No module named 'app.core.llm_client_direct'

# Basic self-healing trial
from app.self_healing_with_mcp import SelfHealingExecutor

executor = SelfHealingExecutor(framework_root=Path("./my-framework"))
success, logs, attempts = executor.run_trial_with_real_time_healing(
    script_content=test_script,
    test_url="https://app.com",
    max_retries=2
)

# Check if healing was successful
if success and attempts:
    print(f"✅ Healed after {len(attempts)} attempts")

from app.self_healing_executor import run_trial_with_self_healing

success, logs, attempts = run_trial_with_self_healing(
    script_content=script,
    framework_root=Path("./framework"),
    max_retries=3,
    headed=True  # Show browser for debugging
)

from app.core.mcp_client import (
    get_microsoft_docs_mcp,
    get_github_mcp,
    get_filesystem_mcp
)

# Search official Playwright docs
docs_mcp = get_microsoft_docs_mcp()
results = docs_mcp.search_docs("getByRole selector")

# Find test patterns in GitHub
github_mcp = get_github_mcp()
patterns = github_mcp.search_code("microsoft/playwright", "page.getByRole")

# Safe file operations
fs_mcp = get_filesystem_mcp()
fs_mcp.safe_write_file(Path("test.spec.ts"), content, backup=True)

from app.core.llm_client_direct import DirectCopilotClient

# Initialize client
client = DirectCopilotClient(temperature=0.2, model="gpt-4")

# Generate test code
response = client.invoke("Generate a Playwright login test")
print(response.content)

# Self-healing
from app.core.llm_client_direct import ask_llm_to_self_heal
fixed_script = ask_llm_to_self_heal(failed_script, error_logs, ui_crawl_data)