Methods and Techniques for Testing AI-Based Systems: ISTQB AI Testing

For those seeking a quick overview of methods and techniques for testing AI-based systems, here is a summary of pages 65-72 of the ISTQB AI Testing syllabus. Do not rely upon it as preparation for the ISTQB AI Testing exam – this is a quick summary to help you gauge your interest in this important testing topic.

AI-based systems require innovative testing methodologies to address their inherent complexities, non-deterministic behavior, and reliance on large datasets. Below is a detailed exploration of the methods and techniques that ensure robust testing for such systems.

Adversarial Testing and Data Poisoning

Adversarial testing identifies vulnerabilities by introducing inputs designed to mislead or disrupt the AI system. Data poisoning involves injecting corrupt or manipulated data into the training process to degrade performance.

Testing Techniques:

• Simulate adversarial attacks to identify system weaknesses.
• Test with noisy or deliberately flawed data to assess robustness.
• Verify the system’s ability to detect and recover from corrupted inputs.

Applications:

• Image recognition systems, where small pixel modifications can lead to incorrect classifications.
• Fraud detection models, where adversarial examples aim to bypass safeguards.

Pairwise Testing

Pairwise testing systematically generates test cases to cover all possible combinations of input parameter pairs. This technique reduces the number of tests while maintaining thorough coverage.

Benefits:

• Efficient identification of interactions between input parameters.
• Effective for systems with a high number of configurable inputs.

Example Use Case:

Testing a recommendation engine by combining different user demographics and preferences to identify performance gaps.

Back-to-Back Testing

This method compares the outputs of two functionally equivalent systems, typically a new and a legacy system or two versions of the same AI model.

Testing Approach:

• Compare outputs using identical inputs.
• Identify discrepancies that indicate potential issues in the new system.

When to Use:

• Migrating from legacy AI systems to modern frameworks.
• Validating enhancements to existing models.

A/B Testing

A/B testing evaluates two system versions in real-world environments, measuring user behavior or performance metrics to determine which version performs better.

Steps:

1. Deploy two versions (A and B) simultaneously to a subset of users.
2. Collect feedback and performance data.
3. Use statistical analysis to determine significant differences.

Common Applications:

• Optimizing user interfaces in AI-driven applications.
• Comparing the accuracy of machine learning models.

Metamorphic Testing (MT)

Metamorphic testing identifies relationships between inputs and expected outputs, generating new test cases based on these transformations. It is particularly effective for systems without a defined test oracle.

Testing Example:

For a weather prediction model, modifying an input parameter (e.g., location altitude) should produce predictable changes in output (e.g., lower temperature).

Advantages:

• Addresses challenges in non-deterministic systems.
• Reduces reliance on pre-defined datasets.

Experience-Based Testing

Experience-based testing leverages tester expertise to explore the system’s behavior without pre-designed cases. It often involves exploratory testing or exploratory data analysis (EDA).

Testing Focus:

• Detect unanticipated behaviors or edge cases.
• Evaluate how the system handles incomplete or noisy data.

Example:

Testing an AI chatbot for its ability to respond accurately to ambiguous or incomplete user queries.

Selecting Test Techniques for AI Systems

Choosing the right testing technique depends on several factors:

• System Complexity: For highly complex models, experience-based or metamorphic testing might be more effective.
• Data Sensitivity: Adversarial testing is critical for systems prone to malicious attacks.
• Non-Determinism: Techniques like metamorphic testing address the challenges of systems with unpredictable behavior.

Recommendations:

• Combine multiple techniques to ensure comprehensive testing.
• Regularly update test strategies to adapt to evolving models and data.

Learn More About AI Testing

• Introduction to AI
• Testing AI Systems Overview
• Testing AI-specific Quality Characteristics
• Methods and Techniques for Testing AI-Based Systems
• Test Environments for AI-Based Systems
• Using AI for Testing