Introduction to Data Management

Data is the "fuel" for testing - AngusTester provides a full lifecycle data management solution, enabling test data to:
✅ Generate on demand ✅ Flexible reuse ✅ Secure and reliable ✅ Intelligent and efficient
Helping teams improve testing efficiency by 3x and reduce 80% of data preparation time!

What is Test Data? The "Raw Material" for Quality Verification

Test data is the core resource for validating software functionality, including:

Three Advantages of AngusTester Data Management:

1. One-click Generation: Automatically create massive test data
2. Intelligent Reuse: Share data assets across projects
3. Dynamic Updates: Maintain data validity in real-time

"Without good test data, even the most comprehensive test cases cannot deliver value - AngusTester makes data an accelerator for quality assurance"

Four-Dimensional Data System: Comprehensive Test Coverage

1. Variable Management - Flexible Data Units

Application Scenarios:

• Global configuration parameters
• Dynamically calculated values
• Environment-specific configurations

2. Datasets - Structured Data Warehouse

E-commerce Dataset Example:

| UserID | Product Name | Price | Quantity | Order Status |
|--------|--------------|-------|----------|--------------|
| U1001 | Smartphone | 5999 | 1    | Paid        |
| U1002 | Bluetooth Headphones | 399  | 2    | Pending Shipment |
| U1003 | Smart Watch | 1299 | 1    | Cancelled   |

Dataset Management:

3. File Center - Unstructured Data Hub

Supported File Types:

File Operations:

• 📤 One-click import/export
• 🔍 Full-text search
• 🔗 Version tracking
• 👥 Team sharing

4. Data Sources - Bridges to External Systems

Connection Capabilities:

Intelligent Parameterization: The Data-Driven Testing Revolution

1. Variable Injection - Static Data Application

2. Dataset-Driven - Batch Testing Solution

Data-Driven Testing Process:

3. Mock Functions - Dynamic Data Generation

Common Mock Functions:

 {
    "name": "@Name()",
    "email": "@Email()",
    "phone": "@Mobile()",
    "address": "@Address()",
    "hobbies": [ "reading", "hiking",  "cooking" ]
}

4. Sampling Extraction - Intelligent Data Flow

Cross-Step Data Transfer:

Four-Step Data Management Workflow

Step 1: Data Design

Step 2: Data Generation

Intelligent Generation Methods:

Step 3: Data Application

Test Execution Process:

Step 4: Data Governance

Data Lifecycle Management: