👥 Behavioural Testing

ai+me's behavioural testing simulates real-world user interactions with your AI systems to ensure they perform as expected in actual usage scenarios. This approach goes beyond security testing to validate user experience, functionality, and behavioural alignment with your business objectives.

🎯 What is Behavioural Testing?

Behavioural testing is a comprehensive approach to validating AI system behavior by simulating realistic user interactions and scenarios. Think of it as quality assurance for AI—we systematically test how your AI responds to various user inputs and situations to ensure it meets expectations.

🔍 Key Concepts

User-Centric Testing

• Purpose: Validate AI behavior from a user perspective
• Method: Simulate realistic user interactions and workflows
• Goal: Ensure positive user experiences and outcomes

Functional Validation

• Purpose: Verify AI system functionality and capabilities
• Method: Test core features and business logic
• Goal: Ensure AI performs intended functions correctly

Behavioural Alignment

• Purpose: Validate AI behavior against business objectives
• Method: Test responses against defined business rules and policies
• Goal: Ensure AI stays within intended boundaries and scope

🏗️ How Behavioural Testing Works

🔄 Testing Process

Step 1: Scenario Definition

1. Use Case Analysis: Identify key user scenarios and workflows
2. User Journey Mapping: Map typical user interaction patterns
3. Edge Case Identification: Identify unusual but valid user inputs
4. Error Scenario Definition: Define potential error conditions

Step 2: Test Generation

1. Conversation Flow Creation: Generate realistic conversation flows
2. Input Variation: Create variations of user inputs
3. Context Simulation: Simulate different conversation contexts
4. Scenario Coverage: Ensure comprehensive scenario coverage

Step 3: Test Execution

1. Automated Testing: Run behavioural tests against your AI system
2. Response Collection: Capture AI responses to various inputs
3. Performance Monitoring: Track response times and system behavior
4. Error Handling: Manage test failures and edge cases

Step 4: Response Evaluation

1. Behavioural Assessment: Evaluate responses for expected behavior
2. Quality Validation: Assess response quality and relevance
3. Policy Compliance: Check adherence to business policies
4. User Experience: Evaluate from a user perspective

Step 5: Analysis and Reporting

1. Behavioural Reports: Generate detailed behavioural analysis reports
2. Quality Metrics: Calculate quality and performance metrics
3. Improvement Recommendations: Suggest specific improvements
4. Trend Analysis: Track behavioural patterns over time

🎯 Testing Categories

User Interaction Testing

Happy Path Testing

• Description: Test normal, expected user interactions
• Scenarios: Standard user workflows and common use cases
• Goals: Ensure smooth user experiences and correct functionality
• Examples: Order processing, account management, information retrieval

Edge Case Testing

• Description: Test unusual but valid user inputs
• Scenarios: Boundary conditions, unexpected inputs, complex requests
• Goals: Ensure graceful handling of edge cases
• Examples: Very long inputs, unusual formatting, complex queries

Error Handling Testing

• Description: Test AI response to user errors and invalid inputs
• Scenarios: Invalid data, malformed requests, user mistakes
• Goals: Ensure helpful error messages and graceful error recovery
• Examples: Invalid email formats, missing required fields, typos

Accessibility Testing

• Description: Test AI accessibility for diverse users
• Scenarios: Different user abilities, languages, and backgrounds
• Goals: Ensure inclusive and accessible AI interactions
• Examples: Clear language, alternative formats, cultural sensitivity

Functional Testing

Core Functionality

• Description: Test primary AI capabilities and features
• Scenarios: Main business functions and key features
• Goals: Ensure core functionality works correctly
• Examples: Data processing, decision making, content generation

Integration Testing

• Description: Test AI integration with other systems
• Scenarios: API interactions, data flows, system dependencies
• Goals: Ensure seamless integration and data consistency
• Examples: Database connections, third-party APIs, external services

Performance Testing

• Description: Test AI system performance under various conditions
• Scenarios: High load, concurrent users, resource constraints
• Goals: Ensure acceptable performance and reliability
• Examples: Response time validation, throughput testing, stress testing

Scalability Testing

• Description: Test AI system behavior under scale
• Scenarios: Increased load, growing data, expanding user base
• Goals: Ensure system scales appropriately
• Examples: Load balancing, resource utilization, capacity planning

Behavioural Alignment Testing

Policy Compliance

• Description: Test adherence to business policies and rules
• Scenarios: Policy boundaries, rule enforcement, compliance requirements
• Goals: Ensure AI follows defined policies and constraints
• Examples: Data privacy, content moderation, access control

Scope Validation

• Description: Test AI behavior within defined scope
• Scenarios: Scope boundaries, unauthorized requests, out-of-scope queries
• Goals: Ensure AI stays within intended boundaries
• Examples: Role-based access, feature limitations, domain restrictions

Ethical Behavior

• Description: Test AI behavior for ethical considerations
• Scenarios: Bias detection, fairness, transparency, accountability
• Goals: Ensure ethical and responsible AI behavior
• Examples: Bias testing, fairness validation, transparency assessment

Cultural Sensitivity

• Description: Test AI behavior across different cultures and contexts
• Scenarios: Cultural variations, language differences, regional considerations
• Goals: Ensure culturally appropriate and sensitive responses
• Examples: Language localization, cultural context, regional compliance