Course

ISTQB Certified in AI Testing

This introductory certification course to Artificial Intelligence (AI), gives you a broad insight into the AI methods used to test AI-based solutions and how AI-based solutions can be used to test other IT-systems.

Learning objective

This fundamental course to AI, Artificial Intelligence, gives you a broad insight into the AI methods used to test AI-based solutions and how AI-based solutions can be used to test other IT-systems. Furthermore the following outcome:

  • Understand the current state and expected trends of AI
  • Experience the implementation and testing of a ML model and recognize where testers can best influence its quality
  • Understand the challenges associated with testing AI-Based systems, such as their self-learning capabilities, bias, ethics, complexity, non-determinism, transparency and explainability
  • Contribute to the test strategy for an AI-Based system
  • Design and execute test cases for AI-based systems
  • Recognize the special requirements for the test infrastructure to support the testing of AI-based systems
  • Understand how AI can be used to support software testing

Target audience

The ISTQB Certified in AI Testing aimed at people who are seeking to extend their understanding of artificial intelligence and/or deep (machine) learning, most specifically testing AI based systems and using AI to test.

Prerequisites

To achieve the CT-AI certification, candidates must hold the ISTQB® Certified Tester Foundation Level (CTFL) certificate, but now a prerequisite to attend the course.

Furthermore, we recommend that you have:

  • Knowledge and understanding of programming language – Java/Python/R
  • Knowledge and understanding of statistics
  • Experience with software development and testing

Course and Exam format

Over the course of 4 intensive days, you will be taught in the subjects included in the exam. The course contains both a theoretical review, practical exercises, and discussion. There will be a high degree of participant involvement.

Prior to course start, expect to prepare by reading approximately 10 hours from the curriculum. During the course you should expect 2 hours of homework every day after class.

Exam format:

The exam is an official ISTQB-exam. The 1-hour exam has 40 multiple choice questions, and no assistance is permitted. You must answer at least 65% of the questions correctly to pass. Participants that take the exam not in their spoken language, will receive additional 25% time, and will have 15 minutes more, or a total of 75 min.

In-House training?

If you are more than 5 people from same organisation, it can be beneficial to consider the course as in-house training. We conduct the course exclusively for your employees, either as standard as described or tailored to your needs.

Advantages of in-house training

  • Financial savings for more than 5 people
  • Intensive exchange of experiences and knowledge sharing
  • Employees gain a common understanding of the subject
  • Opportunity for unique customization based on your own methods and processes

Contact Us

Contact us to learn more about how we can customize a program specifically for your company.

In-house training or questions?

If you are more than 5 people or just need some help contact us at info@triforkqi.com

Contact

Course content

1. Introduction to AI

  • Definition of AI and AI Effect
  • Narrow, General and Super AI
  • AI-Based and Conventional Systems
  • AI Technologies
  • AI Development Frameworks
  • Hardware for AI-Based Systems
  • AI as a Service (AIaaS)
  • Pre-Trained Models
  • Standards, Regulations and AI

2. Quality Characteristics for AI-Based Systems

  • Flexibility and Adaptability
  • Autonomy
  • Evolution
  • Bias
  • Ethics
  • Side Effects and Reward Hacking
  • Transparency, Interpretability and Explainability
  • Safety and AI

3. Machine Learning (ML) – Overview

  • Forms of ML
  • ML Workflow
  • Selecting a Form of ML
  • Factors involved in ML Algorithm Selection
  • Overfitting and Underfitting

4. ML – Data

  • Data Preparation as part of the ML Workflow
  • Training, Validation and Test Datasets in the ML Workflow
  • Dataset Quality Issues
  • Data quality and its effect on the ML model
  • Data Labelling for Supervised Learning

5. ML Functional Performance Metrics

  • Confusion Matrix
  • Additional ML Functional Performance Metrics for Classification, Regression and Clustering
  • Limitations of ML Functional Performance Metrics
  • Selecting ML Functional Performance Metrics
  • Benchmark Suites for ML

6. ML – Neural Networks and Testing

  • Neural Networks
  • Coverage Measures for Neural Networks

7. Testing AI-Based Systems Overview

  • Specification of AI-Based Systems
  • Test Levels for AI-Based Systems
  • Test Data for Testing AI-Based Systems
  • Testing for Automation Bias in AI-Based Systems
  • Documenting an ML Model
  • Testing for Concept Drift
  • Selecting a Test Approach for an ML System

8. Testing AI-Specific Quality Characteristics

  • Challenges Testing Self-Learning Systems
  • Testing Autonomous AI-Based Systems
  • Testing for Algorithmic, Sample and Inappropriate Bias
  • Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
  • Challenges Testing Complex AI-based Systems
  • Testing the Transparency, Interpretability and Explainability of AI-based Systems
  • Test Oracles for AI-Based Systems
  • Test Objectives and Acceptance Criteria

9. Methods and Techniques for the Testing of AI-Based Systems

  • Adversarial Attacks and Data Poisoning
  • Pairwise Testing
  • Back-to-Back Testing
  • A/B Testing
  • Metamorphic Testing
  • Experience-Based Testing of AI-Based Systems
  • Selecting Test Techniques for AI-Based Systems

10. Test Environments for AI-Based Systems

  • Test Environments for AI-Based Systems
  • Virtual Test Environments for Testing AI-Based Systems

11. Using AI for Testing

  • AI Technologies for Testing
  • Using AI to Analyze Reported Defects
  • Using AI for Test Case Generation
  • Using AI for the Optimization of Regression Test Suites
  • Using AI for Defect Prediction
  • Using AI for Testing User Interfaces

introduction

meet the trainer

Viepul Kocher

Viepul Kocher is an ex-Adobe engineer and IIT alumnus with a 25-year experience in Software Development and Testing industry. Besides being the creator of ISTQB Certified AI Testing and a former certification in AI testing, Viepul is also President of the Indian Testing – ISTQB Board, Convenor of STeP-IN forum and National Convenor of Indica Academy.

ISTQB Certified in AI Testing

ISTQB Certified in AI Testing

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