ISTQB AI Testing

Kesto: 4.0 pv

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How do you test AI-based systems? What are the challenges for self-learning systems? How can you use AI to improve your testing?

Artificial Intelligence and Machine Learning in particular are used more and more in everyday applications and systems. The ISTQB® AI Testing (CT-AI) certification extends understanding of artificial intelligence and/or deep (machine) learning, most specifically testing AI-based systems and using AI in testing, and gives answers to the above questions.

After the course, you can take the ISTQB certification exam. The multiple choice exam has 40 questions, and in order to pass the exam, you need to score 31 out of 47 possible points. The duration of the exam is one hour; non-native English-speakers are allowed 15 minutes extra time.

Target group:

The Certified Tester AI Testing certification is aimed at anyone involved in testing AI-based systems and/or AI for testing. This includes people in roles such as testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers. This certification is also appropriate for anyone who wants a basic understanding of testing AI-based systems and/or AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants.

Prerequisites:

The participants must hold the ISTQB/ISEB Foundation Certificate in Software Testing. As the course material and the certification exam are in English, the participants are expected to have good command of English language.

Ohjelma

Course contents:

 

Chapter 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

 

Chapter 2: Quality Characteristics for AI-Based Systems

Flexibility and Adaptability

Autonomy

Evolution, Bias and Ethics

Side Effects and Reward Hacking

Transparency, Interpretability and Explainability

Safety and AI

 

Chapter 3: Machine Learning (ML) – Overview

Forms of ML and ML Workflow

Selecting a Form of ML

 

Chapter 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

Chapter 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 Performance

 

Chapter 6: ML – Neural Networks and Testing

Neural Networks

Coverage Measures for Neural Networks

 

Chapter 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 AI Component

Testing for Concept Drift

Selecting a Test Approach for an ML System

 

Chapter 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

 

Chapter 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 (MT)

Experience-based testing of AI-based Systems

Selecting Test Techniques for AI-based Systems

 

Chapter 10: Test Environments for AI-Based Systems

Test Environments for AI-Based Systems

Virtual Test Environments for Testing AI-Based Systems

 

Chapter 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

 

Peruutusehdot

If you can not participate this course, you can send someone else instead of you. If cancellation is done less than 21 days before the course start, we will charge 50% of the price. In case of no show without any cancellation, we will charge the whole price. Cancellation fee will also be charged in case of illness.

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