AI Test Platform Developer Guide#

Purpose#

Build an AI-powered automated testing platform for internal company use, leveraging DeepSeek LLM and LangChain framework to achieve intelligent test case generation, automated script creation, and test execution management. The platform focuses on API testing (Pytest+Requests) and UI testing (Playwright) with Docker-based isolation and authorization control.

Technology Stack#

LayerTechnologyVersionPurposeFrontendVue3^3.3.0Reactive UI frameworkFrontend UIElement Plus^2.4.0UI component libraryState ManagementPinia^2.1.0Global state managementBackendFastAPI^0.104.0Async web frameworkAI FrameworkLangChain^0.1.0LLM orchestrationLLMDeepSeekAPICore AI capabilitiesAPI TestingPytest^7.4.0Test frameworkAPI Reportingpytest-json-report^1.5.0JSON report generationUI TestingPlaywright^1.40.0Browser automationDatabaseMySQL8.0Relational databaseVector DBChroma^0.4.0Vector retrievalDeploymentDockerLatestContainerization

Architecture Overview#

System Layers#

text

【Frontend Layer】Vue3 + Element Plus + Pinia
      ↓ (Polling for progress)
【API Layer】FastAPI + Auth Interceptor
      ↓
【Business Layer】Auth Management, Test Design, Execution, Reporting
      ↓
【AI Layer】LangChain + DeepSeek + RAG (Chroma)
      ↓
【Test Engine Layer】Pytest (API) + Playwright (UI)
      ↓
【Data Layer】MySQL (Business) + Chroma (Vectors)

Core Modules#

1. Authorization Management Module#

Permission Types:

• all - Full functionality
• generate - Case/script generation only
• execute - Script execution only

Encryption:

• Algorithm: AES
• Key generation: "yanghua" + timestamp + "360sb"

Authorization Flow:

text

Request with auth_code → AuthInterceptor → Verify validity/expire/count/permission
→ Pass: Allow access + increment usage count
→ Fail: Return 401/403

Key Services:#

• AuthCodeService: CRUD, validation, count updates
• AuthService: Global request interceptor

2. AI Generation Module#

Supported Document Formats:

• Word (.docx) - python-docx
• Excel (.xlsx) - openpyxl
• PDF (.pdf) - PyPDF2/pdfplumber
• Markdown (.md) - markdown

Core Chains:

• TestCaseChain: Generate test cases from documents
• ApiScriptChain: Generate Pytest+Requests scripts
• UiScriptChain: Generate Playwright scripts

AI Configuration:

• No QPS limit
• Retry: 2 times on failure
• Timeout: 30 seconds
• Expected usage: ≤20 calls/day

3. API Automation Module#

Integration:

• Execute via pytest.main()
• Parse results using pytest-json-report

Environment:

• Docker container isolation
• Dependencies managed via requirements.txt

Script Features:

• Save, edit, categorize, batch manage
• Configure test environment, headers, global params
• Real-time debugging with request/response

4. UI Automation Module#

Playwright Configuration:

• Headless mode (no UI)
• Screenshot on every test case (success + failure)
• Trace on failure (HTML trace viewer)
• Support Chrome and Edge

Capabilities:

• AI-generated element locators (ID/XPath)
• Flow script generation (login, click, input, assert, screenshot)
• Multi-browser compatibility

5. Test Execution Module#

Features:

• Single or batch script execution
• Real-time log capture
• Execution timeout control
• Failure retry mechanism
• Historical record management

6. Test Report Module#

Auto-generation:

• HTML reports after execution
• Export to HTML/PDF
• AI analysis of failures (simple log analysis)

AI Analysis Prompt:

text

Analyze the following test execution log and identify the failure reason:
{execution_log}

Provide:
1. Main failure cause
2. Possible problem location
3. Suggested solution

Keep it concise and highlight key information.

7. System Configuration Module#

Configuration Management:

• DeepSeek API settings
• Environment URLs
• System initialization

Features:

• Operation logs
• Authorization usage logs
• AI call logs
• Automatic data backup

Database Schema#

Core Tables#

1. auth_codes - Authorization codes
   • Fields: id, code(encrypted), permission, expire_time, use_count, max_count, is_active
2. test_cases - Test cases
   • Fields: id, title, content, type(api/ui), created_by, create_time
3. auto_scripts - Automated scripts
   • Fields: id, name, content, type(api/ui), status, created_by, create_time, update_time
4. execute_records - Execution records
   • Fields: id, script_id, auth_code, result(success/fail), log, execute_time, duration
5. test_reports - Test reports
   • Fields: id, record_id, report_content, file_path, ai_analysis, create_time
6. task_progress - Task progress tracking
   • Fields: id, task_id, task_type(generate/execute), status, progress, message, result_data

See references/architecture.md for complete SQL definitions.

API Design#

Unified Response Format#

json

{
  "code": 200,
  "msg": "success",
  "data": {}
}

Common Parameters#

• auth_code (required) - Authorization code for all core endpoints

Core Endpoints#

Authorization:

• POST /admin/add_auth - Create authorization code
• POST /auth/verify - Verify authorization
• GET /auth/list - List authorization codes

AI Generation:

• POST /generate/case - Generate test cases
• POST /generate/api - Generate API scripts
• POST /generate/ui - Generate UI scripts
• GET /progress/{task_id} - Get task progress

Automation Management:

• POST /script/save - Save script
• GET /script/list - List scripts
• GET /script/{id} - Get script details

Test Execution:

• POST /execute/run - Run test script
• GET /execute/record - Get execution records

Report Management:

• GET /report/generate - Generate report
• GET /report/export - Export report

System Configuration:

• GET /system/config - Get system config
• POST /system/config - Update system config

Frontend Progress Polling#

Implementation:

• State management: Pinia
• Polling interval: Every 2 seconds
• Progress types:
  • AI generation (parsing → vectorizing → generating → complete)
  • Script execution (running → collecting → reporting)

Polling Endpoint:

bash

GET /api/progress/{task_id}
# Response
{
  "status": "processing|completed|failed",
  "progress": 50,
  "message": "Generating test cases..."
}

Deployment#

Docker Compose Setup#

yaml

version: '3.8'
services:
  mysql:
    image: mysql:8.0
    environment:
      MYSQL_ROOT_PASSWORD: root123
      MYSQL_DATABASE: ai_test_platform
    volumes:
      - mysql_data:/var/lib/mysql
    ports:
      - "3306:3306"

  app:
    build: .
    ports:
      - "8000:8000"
    depends_on:
      - mysql
    volumes:
      - ./data:/app/data
    environment:
      - DATABASE_URL=mysql+pymysql://root:root123@mysql:3306/ai_test_platform
      - DEEPSEEK_API_KEY=your_api_key

volumes:
  mysql_data:

Data Persistence#

• MySQL: Docker Volume
• Scripts: ./data/scripts/
• Reports: ./data/reports/
• Screenshots: ./data/screenshots/
• Vectors: ./data/chroma/

When to Use This Skill#

Use this skill when:

• Developing the AI test platform backend or frontend
• Implementing test case generation features
• Creating API or UI automated test scripts
• Integrating AI models (DeepSeek) with LangChain
• Setting up authorization and security mechanisms
• Configuring Docker deployment environment
• Debugging test execution issues
• Designing test report generation

Usage Guidelines#

Backend Development#

1. Follow FastAPI best practices
2. Implement async/await for I/O operations
3. Use Pydantic models for request/response validation
4. Implement proper exception handling
5. Add logging for all operations

Frontend Development#

1. Use Vue3 Composition API
2. Implement proper state management with Pinia
3. Handle loading and error states
4. Implement progress polling for async operations
5. Follow Element Plus component guidelines

AI Integration#

1. Use LangChain for chain orchestration
2. Implement retry logic for API calls
3. Handle timeouts gracefully
4. Cache common responses
5. Monitor API usage

Security Considerations#

• All core endpoints must verify auth_code
• Encrypt authorization codes using AES
• Sanitize all user inputs
• Implement rate limiting (optional)
• Never expose sensitive data in logs

Project Structure#

text

ai-test-platform/
├── backend/
│   ├── app/
│   │   ├── api/              # API endpoints
│   │   ├── core/             # Core security and config
│   │   ├── models/           # Database models
│   │   ├── schemas/          # Pydantic schemas
│   │   ├── services/         # Business logic
│   │   │   ├── auth.py       # Authorization service
│   │   │   ├── ai.py         # AI generation service
│   │   │   ├── execute.py    # Execution service
│   │   │   └── report.py     # Report service
│   │   └── main.py           # FastAPI app
│   ├── tests/                # Test files
│   ├── Dockerfile
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── components/       # Vue components
│   │   ├── pages/            # Page components
│   │   ├── stores/           # Pinia stores
│   │   ├── api/              # API calls
│   │   └── main.js           # Entry point
│   └── package.json
├── data/                     # Persistent data
│   ├── scripts/
│   ├── reports/
│   ├── screenshots/
│   └── chroma/
├── docs/                     # Documentation
├── scripts/                  # Utility scripts
│   ├── generate_auth.py      # Auth code generator
│   ├── init_db.py            # Database initializer
│   └── setup.sh              # Setup script
├── docker-compose.yml
└── README.md

Development Workflow#

1. Setup Development Environment#

bash

# Clone and setup
git clone <repo>
cd ai-test-platform

# Backend setup
cd backend
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

# Frontend setup
cd ../frontend
npm install

2. Initialize Database#

bash

python scripts/init_db.py

3. Generate Authorization Codes#

bash

python scripts/generate_auth.py

4. Run Development Server#

bash

# Backend
cd backend
uvicorn app.main:app --reload

# Frontend
cd frontend
npm run dev

5. Docker Deployment#

bash

docker-compose up -d

Reference Documents#

• references/architecture.md - Complete system architecture and database design
• references/AI 自动化测试平台 需求规格说明书.docx - Original requirements
• references/AI 自动化测试平台 系统设计说明书.docx - Original system design

Next Steps#

1. Review architecture documentation in references/architecture.md
2. Set up development environment
3. Initialize database schema
4. Generate authorization codes using provided script
5. Start with core backend services (Auth, AI Generation)
6. Implement frontend components
7. Test end-to-end workflows
8. Deploy using Docker Compose

Notes#

• This platform is designed for internal company use only
• All data must be stored internally, no external uploads
• DeepSeek API is the only allowed external model integration
• Authorization codes are mandatory for all core features
• Docker-based isolation ensures security and consistency