Simplified Test Plan Generator

An AI-powered test plan generation system that creates high-quality prompts for guiding AI assistants (ChatGPT, Claude, etc.) to generate targeted test plans and test cases.

🚀 Key Features

🔍 Dual Analysis Modes

• Source Code Mode: Automatically analyze project structure and codebase
• Configuration Mode: Generate prompts from project configuration files (with automatic quality enhancement)

🧪 Comprehensive Test Types

• Unit, Integration, Performance, Security testing
• End-to-End, Stress, Mobile, API, Microservice, Database testing
• Feature-specific and project-level test generation

🎯 Smart Template System

• Intelligent template selection based on project type
• Context-aware prompt generation
• Support for multiple technology stacks

⚡ Automatic Quality Enhancement

• Smart inference engine for configuration completion
• Domain knowledge base integration
• No manual configuration required for config mode

📁 Project Structure

prompt_generator/
├── analyzer/          # Project analysis and quality enhancement
├── templates/         # Comprehensive prompt template library  
├── generator/         # Template combination and prompt generation
├── knowledge/         # Domain knowledge base
├── config/           # Configuration templates and settings
└── main.py          # Main execution script

🛠️ Installation

# Install dependencies
pip install -r requirements.txt

📋 Usage

Source Code Mode (Traditional)

Analyze existing projects with source code:

# Generate prompt by test type (recommended)
python main.py --project-path /path/to/project --test-type unit
python main.py --project-path /path/to/project --test-type integration
python main.py --project-path /path/to/project --test-type performance

# Generate test plan for specific feature
python main.py --project-path /path/to/project --create-test-plan-prompt --feature-name auth --test-type security

# Generate test cases from existing test plan
python main.py --project-path /path/to/project --create-test-case-prompt --test-plan-path plan.md

Configuration Mode (No Source Code)

For projects without accessible source code, using configuration files with automatic quality enhancement:

# Basic usage (automatic quality enhancement applied)
python main.py --config-file my_project_config.yaml --test-type unit
python main.py --config-file my_project_config.yaml --test-type integration --verbose

# Feature-specific testing
python main.py --config-file my_project_config.yaml --create-test-plan-prompt --feature-name user_auth --test-type security

Configuration Setup:

1. Copy the template: cp config/project_config_template.yaml my_project_config.yaml
2. Edit the configuration file with your project details
3. Run with --config-file parameter

General Commands

# List all supported test types
python main.py --list-test-types

# List available prompt templates
python main.py --list-prompt-templates

# List project features (source code mode only)
python main.py --project-path /path/to/project --list-features

# Save analysis results
python main.py --project-path /path/to/project --test-type unit --save-analysis

# Verbose output
python main.py --config-file config.yaml --test-type unit --verbose

🧪 Supported Test Types

Category	Test Types	Description
Basic Tests	unit, integration	Core functionality testing
Performance	performance, stress	Load and stress testing
System Tests	e2e, security	End-to-end and security testing
Specialized	mobile, api, microservice, database	Technology-specific testing

🎯 Supported Project Types

• Web Applications: Flask, Django, FastAPI, React, Vue, Angular
• API Services: RESTful APIs, GraphQL, Microservices
• Mobile Applications: iOS, Android, React Native
• Machine Learning: TensorFlow, PyTorch, Scikit-learn
• Federated Learning: NVFlare, PySyft
• Database Systems: SQL, NoSQL, Data warehouses
• Generic Projects: Any software project

📊 Quality Enhancement Features

When using Configuration Mode, the system automatically applies:

🧠 Intelligent Inference Engine

• Automatically infers missing project details
• Suggests test scenarios based on technology stack
• Identifies potential risk areas

📚 Domain Knowledge Base

• Industry best practices integration
• Technology-specific testing patterns
• Quality metrics and standards

🔍 Quality Assessment

• Multi-dimensional quality scoring
• Completeness and specificity analysis
• Confidence level evaluation

💡 Example Workflow

Source Code Analysis

$ python main.py --project-path ./my-web-app --test-type integration

🔍 Analyzing project: ./my-web-app
✅ Integration test prompt generated: prompt_result/test_plan_prompt_integration.md
📝 Test type: integration
📄 Template used: integration_test_prompt

💡 Usage suggestions:
   1. Copy prompt content to ChatGPT/Claude etc.
   2. AI will generate detailed integration test plan
   3. Ask follow-up questions about specific scenarios

Configuration-Based Generation

$ python main.py --config-file project_config.yaml --test-type unit --verbose

🎯 启用质量增强模式...
✅ 质量增强完成，评分: 0.73
📊 应用的增强技术: 智能推断, 领域知识库
✅ Unit test prompt generated: prompt_result/test_plan_prompt_unit_config.md
🔧 Mode: Configuration-based

🔧 Configuration Template

Create my_project_config.yaml based on config/project_config_template.yaml:

project_info:
  name: "My Web Application"
  description: "E-commerce platform with React frontend and Django backend"
  type: "web_application"

tech_stack:
  languages: ["Python", "JavaScript"]
  frameworks: ["Django", "React"]
  databases: ["PostgreSQL", "Redis"]

core_modules:
  - name: "User Authentication"
    description: "Login, registration, password reset"
  - name: "Product Catalog"
    description: "Product listing, search, filtering"
  - name: "Shopping Cart"
    description: "Add/remove items, checkout process"

📈 Output Examples

Generated prompts are optimized for AI assistants:

✅ Integration test prompt generated: prompt_result/test_plan_prompt_integration_config.md
📝 Test type: integration
📄 Template used: integration_test_prompt
🔧 Mode: Configuration-based

💡 Usage suggestions:
   1. Copy prompt content to ChatGPT/Claude etc. AI assistants
   2. AI assistants will generate detailed integration test plan
   3. You can ask follow-up questions about specific scenarios
   4. Generated test cases are based on configuration information

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature-name
3. Commit changes: git commit -am 'Add feature'
4. Push to branch: git push origin feature-name
5. Submit pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Ready to generate high-quality test plans? Start with a simple command:

python main.py --config-file config/project_config_template.yaml --test-type unit