ISTQB AI Tester | Specification for Testing AI Based System | AI Tester Certification | AI Tutorials

Summary

This tutorial delves into the challenges of specifying AI-based systems for testing. It highlights the complexity of defining precise requirements for AI systems, which are often defined by high-level business goals and predictions rather than specific functions. The script explores the exploratory nature of AI development, the probabilistic behavior of AI, and how AI must replicate human behaviors, all of which complicate the testing process. It emphasizes the difficulty in documenting AI specifications due to their inherent flexibility, unpredictability, and the need for adaptable quality characteristics, leaving testers to navigate these evolving challenges.

Takeaways

• 😀 Specifications for AI-based systems are often high-level and may lack precise details, making it difficult for testers to define exact requirements.
• 😀 AI systems' specifications are typically based on business goals and predictions, which are more exploratory and less defined than conventional systems.
• 😀 The accuracy of AI-based systems is often unknown until independent tests are conducted, making it challenging to set definitive specifications early on.
• 😀 AI systems require tolerance in their predictions due to their probabilistic nature, meaning outputs can vary slightly while still being acceptable.
• 😀 Specifying AI systems to replicate human behavior can be difficult, as human actions can be unpredictable and vary greatly from day to day.
• 😀 AI systems used in applications like natural language processing and computer vision require increased flexibility in handling diverse user interactions.
• 😀 Documenting all possible interactions in AI systems, especially those involving human behavior, is a significant challenge due to their dynamic nature.
• 😀 The exploratory development approach in AI projects makes it challenging to set predetermined acceptance criteria, leading to evolving specifications.
• 😀 AI systems are often evaluated based on their ability to adapt, evolve, and perform autonomously, but these qualities are difficult to define and test.
• 😀 Testers of AI-based systems must be flexible and adaptable, as the systems they are testing may evolve and produce varying results based on the training data and real-world interactions.

Q & A

What is the primary focus of Chapter 7 in the AI tester certification tutorial?

Chapter 7 focuses on the test-related concepts of AI-based systems, covering topics such as specification, test levels, data requirements, automation bias, model testing, and selecting a test approach for AI systems.

What are specifications in the context of AI-based system testing?

Specifications refer to the requirements, design, and any other foundational information that testers can use to derive test cases. They provide a basis to check whether the actual behavior of the AI system aligns with the expected behavior.

Why can it be difficult to define clear specifications for AI-based systems?

Defining clear specifications for AI-based systems is challenging because they are often developed in an exploratory manner. Requirements are typically high-level and based on desired predictions or business goals rather than specific, detailed functionalities.

What is the 'test oracle problem' in AI system testing?

The test oracle problem arises when the specifications are incomplete or lack testability, making it impossible to determine whether the system's behavior is correct, as there is no clear reference point to compare actual results against.

How does the accuracy of AI systems complicate specification writing?

AI system accuracy is often unknown until independent tests are conducted. The exploratory development approach means that by the time the desired acceptance criteria are determined, implementation may already be in progress, making it difficult to finalize precise specifications.

What role does tolerance play in specifying AI system requirements?

Tolerance is crucial in AI system specifications because AI systems often function probabilistically. This means that outcomes might deviate slightly from expectations, and specifying a range for acceptable accuracy allows for flexibility while still aiming to meet business goals.

What challenge arises when AI systems are used to replicate human behaviors?

When AI systems are used to replicate human behaviors, it can be difficult to define precise behavior requirements. Human actions are unpredictable, and AI might not always behave in exactly the same way as a human, making it challenging to set specific performance expectations.

Why is flexibility important in AI system specifications?

Flexibility is important because AI systems, such as those used for natural language processing or computer vision, need to adapt to various user inputs. Defining all possible interactions in advance is difficult, making flexibility a necessary characteristic in AI system specifications.

What quality characteristics of AI systems need to be considered during testing?

Key quality characteristics that need to be considered during AI system testing include adaptability, flexibility, evolution, and autonomy. These characteristics can be difficult to define and test due to the unpredictable and novel nature of AI systems.

How do the exploratory nature and uncertainty in AI system development impact the tester's role?

The exploratory nature and uncertainty in AI system development mean that testers need to remain adaptable and be ready to work with high-level specifications, business goals, and tolerances rather than precise, fixed requirements. This makes the tester's role more dynamic and requires an understanding of evolving expectations.