This course is focused on individuals with an interest in, or a need to perform, the testing of AI-Based Systems, especially those working in areas such as autonomous systems, big data, retail, finance, engineering and IT services. This includes people in roles such as system testers, test analysts, test engineers, test consultants, test managers, user acceptance testers, business analysts and systems developers.
Syllabus & Sample Exam
Type: Written test – 40 MC* questions (* MC: Multiple Choice Questions)
1 point per question
Duration: Korean 60 mins / English 75 mins (60 mins + extra 25% for non-native speakers)
Passing Criteria: 65% or more
￦143,000 (Include VAT)
Within the period of reception : 100% refund
Reception closing~The day before the examination : 50% refund
The day of the examination : No Refund
Exam Fee: 20% off the regular examination fees
Audience: All students enrolled in a college/university at the time of exam application (※ Applicable only for full-time students)
How to apply: Upload a copy of certificate of enrollment issued within a week on the 1:1 Bulletin Board on STEN (www.sten.or.kr)
Artificial Intelligence (AI) is already extensively used in a wide variety of computer systems to perform tasks such as classification, recognition, analysis, prediction, planning and management. Typical AI-based systems include self-driving vehicles, search engines, intelligent assistants, smart cities, industrial robots, chatbots, fraud detection, financial trading and big data analytics, among others. The characteristics of many of these systems are that they are complex, probabilistic and non-deterministic. This makes the measurement of quality of such systems particularly challenging. Software testing remains the fundamental approach to assuring the required quality of these systems, but traditional approaches have been found lacking, and approaches specific to AI are required. Even though there are no complete solutions, there is already an extensive body of knowledge to support the creation of a certification that will provide holders with an effective basis for performing the testing of AI-based systems.
The AIT syllabus introduces the variety of types of AI-based systems in use today and explains machine-learning, which is often a key part of these systems. It explains how the setting of acceptance criteria needs to change for these systems, and also show how the characteristics of AI-based systems make testing more difficult than for traditional systems.
The unique development process used for machine learning systems introduces a number of potential areas where mistakes can be made, and defects introduced. The syllabus provides insights into how an independent perspective can help contribute to the most effective implementation of this process and prevent problems such as data bias, overfitting, underfitting and misclassification of input data.
The inclusion of AI makes the testing of AI-Based Systems more challenging from several perspectives. Their probabilistic and non-deterministic nature and inherent complexity makes the derivation of expected results particularly difficult. This creates a test oracle problem that leads to the need for new testing approaches to complement more traditional test techniques.
The syllabus explains how some traditional black box testing techniques, such as combinatorial testing, back-to-back testing and A/B testing, are especially useful for these systems. It then introduces some approaches, specifically focused on AI-Based Systems, such as adversarial testing and metamorphic testing in more detail.
The need for white box testing of traditional systems has long been accepted for critical systems. The use of AI-Based systems in critical situations, such as controlling self-driving cars or providing medical advice suggests that white box approaches will also be needed for these systems. Measures of white box coverage for neural networks are introduced, along with tools to support them.
Finally, the use of AI as the basis of tools to support testing are briefly introduced by looking at examples of the successful application of AI to common testing problems.
Understand the current state of AI and expected advances in the near future;
Interpret and provide guidance on the specification of acceptance criteria for AI-Based Systems;
Contribute to the development process for machine learning systems and suggest opportunities for influencing their quality;
Understand the new challenges of testing AI-Based Systems, such as their complexity and non-determinism;
Contribute to the test strategy for an AI-Based Systems;
Apply black box and white box test design techniques to generate test suites for AI-Based Systems;
Recognize the need for virtual test environments to support the release of complex AI-Based Systems;
Understand the current state of testing supported by AI.
Public Exam: 6 times per year (language: English)
Post-training Exam: For those who get trainings from KSTQB Accredited Training Providers (ATP)