AI and Software Testing Foundation

We recommend attending the ISTQB Foundation Certification course before attending AI and software testing course

Candidates can achieve this certification by passing the following exam(s).

• AIFL: A4Q AI and Software Testing Foundation

The certification exam can be registered and attempted within 3 months of course/module completion at Logitrain training centre on weekdays during normal business hours (excludes public holidays)

• Key Aspects of Artificial Intelligence
• What are Human Intelligence and Artificial Intelligence?
• History of AI
• Symbolic AI
• Sub-symbolic AI
• Some ML Algorithms in More Detail
• Applications and Limits of AI
• General Problems with Testing AI Systems
• Machine Learning Model Training and Testing
• Testing Artificial Intelligence Systems
• AI Test Environments
• Strategies to Test AI-based Systems
• Metrics for Testing AI-based Systems
• Using AI to Support Testing
• AI in Testing
• Applying AI to Testing Tasks and Quality Management
• AI in Component Level Test Automation
• AI in Integration Level or System Level Test Automation
• AI-based Tool Support for Testing

This course is likely to add to the employment related skills of the participants. The skills developed are likely to be used in the course of being an employee or working in a business.

• Types of Intelligence
• Turing Test
• Main Periods of AI History
• Difference between Symbolic and Sub-symbolic AI
• Mathematical Logic and Inference
• Knowledge-based Systems
• Constraint-based Solving Systems for Problem Solving
• Types of Learning
• Examples of Applications of Different Types of Learning
• Machine Learning Algorithms
• Machine Learning Metrics
• Bayesian Belief Networks
• Naïve Bayes classifier
• Support Vector Machine Algorithm
• K-means Algorithm
• Artificial Neural Networks: Perceptron Learning Algorithm
• Activities of Machine Learning
• Possible Biases in AI Systems
• Ethical Issues in AI Systems
• Real-world Inputs
• Self-optimization
• Expert Systems
• Perception of Intelligence
• Model Optimization
• AI System Quality Characteristics
• Bias/Variance Trade Off and the No Free Lunch Theorem
• Drift
• Ethical Considerations
• Automation Bias
• Adversarial Actors
• Acceptance Criteria
• Functional Testing
• External and Internal Validity
• Metamorphic Testing
• A/B Testing
• Evaluation of Real-world Outcomes
• Expert Panels
• Levels of Testing
• Component Testing
• System Integration Testing
• System Testing
• User Acceptance Testing
• Confusion Matrix
• Statistical Significance
• The Oracle Problem
• Test Oracles
• Testing versus Test Automation
• Tasks AI Can Be Applied To
• Tasks AI Cannot Be Applied To
• Using AI for Test Data Generation
• Using AI for Bug Triaging
• Using AI for Risk Prediction and Fault Estimation
• AI in Component Level Test Generation
• AI in System Level Test Generation
• Monkey Testing Versus Fuzz Testing
• AI For Test Generation on the System Level
• AI For Test Selection and Prioritization
• AI For Object Identification and Identifier Selection
• AI For Visual Test Automation
• Relevant Metrics in an AI-Based Testing Approach
• Assess Tool Vendor Claims
• Configuration of the System
• Return on Investment (ROI)
• Effects on Existing Processes
• Sensibility of Test Cases
• Test Case Explosion
• Maintainability
• Severity of the Defects Found

ISTQB Foundation
ISEB Intermediate Certificate
ISTQB Advanced Test Analyst