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Software Test

ISTQB AI Testing - Learn Best Practices and Prepare for the Exam

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will comprehensively learn everything from establishing AI-based system test strategies to designing and executing test cases, and even methods for utilizing AI to support testing. This course is conducted based on ISTQB's AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

4 learners are taking this course

  • edu62481701
ISTQB
Software Test
AI

What you will learn!

  • Understanding the current state and expected trends of AI

  • Gain direct experience with the implementation and testing of machine learning (ML) models, and identify how testers can contribute most effectively to quality.

  • Understand the challenges related to testing AI-based systems (e.g., self-learning capabilities, bias, ethics, complexity, non-determinism, etc.)

  • Developing a Test Strategy for AI-Based Systems

  • Design and execute test cases for AI-based systems

  • Ability to recognize the special requirements of test infrastructure to support AI-based system testing

  • Understand how AI can be used to support software testing

How should we test AI systems? 🤔

Testing traditional systems is well known, but AI-based systems, which are becoming increasingly common and important in our daily lives, present new challenges. This course introduces the core concepts of artificial intelligence (AI), how to determine acceptance criteria, and how to test AI-based systems. These systems have unique characteristics. They are complex (e.g., deep neural networks), based on big data, sometimes self-learning, and sometimes non-deterministic, which brings many new challenges and difficulties to testing.

This course introduces the different types of AI-based systems in use today, explains why machine learning (ML) is a key component of these systems, and shows how easy it is to build ML systems. We will look at how to set acceptance criteria for AI-based systems, why ethical considerations are important, and why the characteristics of AI-based systems make testing more difficult than traditional systems.

Course Introduction

  • To ensure the quality of these systems, we consider three aspects. First, when building a machine learning system, we look at the choices and checks to ensure the quality of the data used for training and prediction. Ideally, there should be no data with inherent bias or incorrect labeling, and most importantly, data should be used that is closely related to the problem you are trying to solve.

  • Next, we will look at several testing techniques suitable for black-box testing of AI-based systems, such as back-to-back testing and A/B testing, and introduce the metamorphic testing technique in detail.

  • Third, we look at how to measure test coverage for neural networks by applying white-box testing.

    The need for virtual testing environments is also illustrated by the case of self-driving cars.

  • Finally, we will consider applying AI to tools that support testing, while looking at some successful cases of AI being applied to testing of traditional systems.

  • This course is very practical, providing students with hands-on experience building and testing different types of machine learning systems through a variety of exercises.

What you will learn

📌Understand the current status and development trends of artificial intelligence.

📌 Experience implementing and testing machine learning models and identify areas where testers can contribute to model quality.

📌 Understand the challenges associated with AI-based systems, including self-learning, bias, ethics, complexity, and non-determinism.

📌 Contribute to testing strategies for AI-based systems.

📌 Design and implement testing best practices for AI-based systems.

📌 Recognize the special requirements of test infrastructure for testing AI-based systems.

📌 Understand how AI-based software can be used to support testing.

💡 Recommended for these people

  • Anyone involved in AI-based systems and testing

  • Testers, Test Analysts, Data Analysts, Test Engineers, Consultants, Test Managers, User Acceptance Testers, Software Developers, etc.

  • Suitable for anyone who wants a basic understanding of AI-based systems or testing, including project managers, quality managers, software development managers, business analysts, and IT leaders.

Lecturer Dr Stuart Reid

  • As CTO of STA Testing Consulting, I have approximately 40 years of experience in the IT industry and am active in development, testing, and training.

  • We are currently researching testing for AI-based systems, applying it to a variety of fields, from safety-critical areas to finance and media.

  • Dr Stuart Reid contributes to the global testing community through a variety of activities.

  • He is the Chair of ISO/IEC SC7 WG26 (Software Testing Working Group), which developed the ISO/IEC/IEEE 29119 series - SW Testing standards, and is also the Co-Chair of ISO/IEC SC42 JWG2 for Artificial Intelligence Testing.

  • Dr Stuart Reid has also led ISO projects related to autonomous systems in the field of software and systems engineering.

  • He is also a co-founder and founding president of ISTQB (International Software Testing Qualifications Board), which promotes software testing qualifications worldwide, and is one of the authors of the ISTQB certification for AI-based systems testing.

Things to note before taking the class

Practice environment

  • Operating System and Version (OS): Windows, macOS, Android, iOS, etc.

Learning Materials

  • A detailed guide is provided with all the information you need.

Are there any required subjects or prerequisites for the course?

  • No programming experience required.


Recommended for
these people

Who is this course right for?

  • Tester, Test Analyst, Data Analyst, Test Engineer, Test Consultant, Test Manager, User Acceptance Testing (UAT) Tester, Software Developer, and all those involved in testing AI-based systems and software utilizing AI.

  • Project Managers, Quality Managers, Software Development Managers, Business Analysts, Operations Team Members, IT Directors, Management Consultants, and others who want a basic understanding of AI-based system testing and/or software testing using AI

  • ISTQB CT-AI Certification Candidates

Need to know before starting?

  • Basic knowledge about software testing, including ISTQB Foundation

Hello
This is

안녕하세요? STA테스팅컨설팅입니다.

STA는 테스팅분야에 기여하고자 모범사례(Best Practice)를 만드는 기업입니다.

STA는 SW 테스팅 분야의 저변 확대와 수준 향상에 기여하며
글로벌 테스팅 서비스 업계의 선두주자로서 모범사례를 개발하는데 주력합니다.

STA 교육센터는?

  • 국내 유일 ISTQB 인증 소프트웨어 테스팅 전문 교육 기관

국내 유일의 ISTQB 인증 교육 기관으로, 국제 표준으로 인정 받고 있는 ISTQB의 지식체계에 기반한 소프트웨어 테스팅 전문 교육 과정을 운영하고 있습니다. 더해 국제 표준(ISO 29119) 워킹 그룹의 한국 대표로, ISTQB와 지속적인 협력을 통해 소프트웨어 테스팅의 국제표준과 그 지식체계를 선도하고 있습니다.

  • 국내 최초 소프트웨어 테스팅 전문 교육 기관

STA테스팅 컨설팅은 국내 최초의 SW테스팅 전문 교육기관으로 2006년부터 현재까지 년간 약 1,400명을 대상으로 소프트웨어 테스트 분야의 교육을 진행 국내 SW 테스팅 교육 분야를 주도하고 있습니다.

  • 국제표준 기반의 자체 개발 교육 과정

소프트웨어 테스팅의 국제 표준인 ISO 29119 를 기반으로, 국제 표준에 따른 실무 지식 습득에 적합한 자체 소프트웨어 테스팅 전문 교육 과정을 통해, 실무에 적용할 수 있는 도메인 기반의 소프트웨어 테스트 전문 지식을 제공합니다.

  • 실무 적용을 고려한 교육 과정

자체 개발한 교육 과정은 해당 직무에 가장 적합한 내용으로 구성하여, 현실적이고 실무에 필요한 기술과 지식을 학습할 수 있습니다. ISTQB 관련 과목의 경우, 교육 이후에 효과 측정과 커리어를 위한 ISTQB 국제 자격증 시험에 도전할 수 있습니다.

  • SW 분야 전문 강사진

수년간 국내 소프트웨어 테스팅 부분의 컨설턴트로 재직하고 있는, 또한 담당하고 있는 과정 개발에 직접 참여하신 전문 강사에 의해 강의가 진행됩니다. 강의를 통해 국제 표준 지식과 국∙내외 Best-Practice, 실 사례에 대한 다양한 경험과 노하우를 학습할 수 있습니다.

  • 지속적인 과정 개발 및 개선

SW테스팅은 계속적으로 발전하고 있고, 이에 요구되는 지식과 기술 역시 계속 변화하고 있습니다. 수년간 SW테스팅 서비스를 통해 얻어진 노하우와 지속적인 R&D 활동을 통해 STA의 교육 과정은 트랜드에 맞추어 계속 새로운 과정을 개발하고, 기존의 과정을 개선해나가고 있습니다. 시장에 발맞춘 전문 SW테스팅 교육 기관으로 현재와 미래를 위해 준비된 교육 서비스를 제공합니다.

Curriculum

All

41 lectures ∙ (12hr 11min)

Published: 
Last updated: 

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