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Fundamentals of AI Testing

This introductory 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 course is 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. It could be the following profiles:

Testers and QA Engineers
Test Managers
Data Scientist
Developers
Project Managers / Scrum Masters / Product Owners

Prerequisites

There is no official prerequisites to attend the course, but it’s a good idea to have basic knowledge and understanding of the following areas:

Programming language – Java/Python/R
Statistics
Experience with software development and testing

Course format

The course contains both a theoretical review, practical exercises, and discussion. There will be a high degree of participant involvement.

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

In-house training or questions?

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

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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

2. Machine Learning (ML) – Overview

Forms of ML
ML Workflow
Selecting a Form of ML

3. ML – Data

Data Preparation as Part of the ML Workflow
Training, Validation and Test Datasets in the ML Workflow
Dataset Quality Issues

4. ML Functional Performance Metrics

Confusion Matrix – hands-on exercise

5. ML – Neural Networks and Testing

Neural Networks
Coverage Measures for Neural Networks

6. Testing AI-Specific Quality Characteristics

Challenges Testing Complex AI-Based Systems
Testing the Transparency, Interpretability and Explainability of AI-Based Systems
Test Oracles for AI-Based Systems
Testing for Concept Drift
Selecting a Test Approach for an ML System
Test Objectives and Acceptance Criteria
Back-to-Back Testing
A/B Testing
Hands-On Exercise: Metamorphic Testing
Experience-Based Testing of AI-Based Systems

7. Using AI for Testing

Using AI for Defect Prediction
Using AI for Testing User Interfaces

INTRODUCTION

Meet our 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.