ISTQB® Foundation - AI For Testers

Our ISTQB® Foundation - AI For Testers training course covers the following Modules:

Module 1: Introduction To A.I

• Definition of A.I and A.I effect
• Narrow, general and super A.I
• A.I-based and conventional systems
• A.I technologies
• A.I Development Frameworks
• Hardware for A.I-based systems
• A.I as a service
• Pre-trained Models
• Standards, regulations and A.I

Module 2: Quality Characteristics Of A.I-Based Systems

• Flexibility and adaptability
• Autonomy
• Evolution
• Bias
• Ethics
• Side effects and reward hacking
• Transparency and interpretability
• Safety and A.I

Module 3: Machine Learning(ML) Overview

• Forms of (ML)
• (ML) Workflow
• Selecting a form of (ML)
• Factors involved in (ML) Algorithm selection
• Overfitting and underfitting

Module 4: (ML) Data

• Data preparation as part of (ML) workflow
• Training, Validation and Test datasets in (ML) workflow
• Dataset quality issues
• Data quality and its effect on the (ML) model
• Data labelling for supervised learning

Module 5: (ML) Functioning Performance Metrics

• Confusion Matrix
• Add (ML) functioning performance metrics for classification, regression and clustering
• Limitations of (ML) functional performance metrics
• Selecting (ML) functional performance metrics
• Benchmark suites for (ML) performance

Module 6: (ML) Neural Networks And Testing

• Neural Networks
• Coverage measure for Neural Networks

Module 7: Testing A.I-Based Systems - Overview

• Specifications of A.I-based systems
• Test levels for A.I-based systems
• Test Data for testing A.I-based systems
• Testing for automation bias in A.I-based systems
• Documenting an A.I-based component
• Testing for concept drift
• Selecting a test approach for an (ML) system 

Module 8: Testing A.I Specific Quality Characteristics

• Challenges testing self-learning systems
• Testing autonomous self-learning systems
• Testing for algorithmic, sample and inappropriate bias
• Challenges testing complex A.I-based systems
• Testing transparency of A.I-based systems
• Test oracles for A.I-based systems
• Test objectives and acceptance criteria

Module 9: Methods And Techniques For The Testing Of A.I-Based Systems

• Attacks and Data poisoning
• Pairwise testing
• A/B testing
• Back-to-back testing
• Metamorphic testing
• Experience-based testing of A.I-based systems
• Selecting test techniques of A.I-based systems

Module 10: Test Environments For A.I-Based Systems

• Test environments for A.I-based systems
• Virtual test environments of A.I-based systems

Module 11: Using A.I For Testing

• A.I technologies for testing
• Using A.I to analyse defect reports
• Using A.I for test case generation
• Using A.I for optimisation of regression test suites
• Using A.I for defect prediction
• Using A.I for testing user interface

Our ISTQB® Foundation - AI For Testers training course will benefit several individuals and organisations, including but not limited to:

• Software Testers
• Test Automation Engineers
• Quality Assurance (QA) Professionals
• Developers
• Test Managers
• AI and Machine Learning Professionals
• Quality Analysts
• Anyone involved in software testing, quality assurance, test automation, or software development can benefit from learning AI testing.

Our ISTQB® Foundation - AI For Testers training course contains the following:

• Pre-reading
• Course Manual
• Quizzes
• Exercises

Our ISTQB® Foundation - AI For Testers training course requires delegates to have experience at the level of our BCS Certificate In Software Testing course.

There is no recommended reading for our ISTQB® Foundation - AI For Testers training course.

ISTQB® Foundation - AI For Testers Exam:

• Format: Multiple choice
• Duration: 1 hour
• Questions: 40
• Pass score: 65% (26 out of 40).

Attendees may enjoy our training course three-day BCS Artificial Intelligence (AI) Foundation course to understand how AI services are produced.

Our ISTQB® Foundation - AI For Testers training course offers several benefits to individuals and organisations, including but not limited to:

• Enhanced Test Automation: AI techniques can improve test automation by enabling intelligent test case generation, adaptive test execution, and self-healing test automation.
• Improved Test Coverage: AI testing techniques can help identify and generate test cases that cover a broader range of scenarios and inputs.
• Early Defect Detection: AI can aid in early defect detection by analysing data and identifying patterns that indicate potential issues.
• Efficient Test Planning and Execution: AI techniques can assist in optimising test planning and execution processes.
• Intelligent Test Result Analysis: AI can help in intelligent analysis of test results, making it easier to identify patterns, trends, and areas of concern.
• Advanced Test Data Generation: AI techniques can assist in generating realistic and diverse test data for testing purposes.
• Adaptability to Evolving Technologies: As AI continues to advance and become more prevalent in software development and testing, learning AI testing ensures that testers stay up-to-date with the latest trends and techniques.