Generative AI in the Service of a Tester

“AI will take my job!” – A random tester in 2022, after learning about the capabilities of ChatGPT.

The idea of artificial intelligence and machine learning has its roots in the work of Alan Turing from the 1950s. Over seventy years later, easy access to AI-based tools has sparked intense discussion, driven by a mix of emotions: excitement about new possibilities and fear of machines replacing humans.

Does widespread access to generative AI change our fundamental role as testers?

This half-day interactive workshop aims to answer that question by exploring the possibilities and implications of using generative AI in software development. We will begin by deepening our understanding of what generative AI is, how it works, and what its limitations are. Then we will move on to practical exercises focused on prompt engineering and how these skills can be applied in our daily work as testers.

Part 1: Introduction to Generative Artificial Intelligence

• Terminology: discussion of key GenAI-related concepts
• Inside GenAI: understanding the architecture and operation of generative models, especially language models
• Limitations of GenAI: challenges such as lack of transparency, data bias, and ethical issues

Part 2: Prompt Engineering

• What is prompt engineering? Introduction and its importance in the context of GenAI
• Prompt engineering techniques: learning different methods for creating effective prompts
• Prompt engineering for testers: focusing on testing needs such as generating test cases, test data, documentation, etc.
• Exercises: hands-on practice using real-world software testing examples

Part 3: Applications of GenAI in a Tester’s Work

• Creativity support: generating test ideas, creating testing personas, writing test scenarios, and other creative tasks
• Work automation: automating repetitive testing tasks such as generating documentation, test cases, and test data
• Code generation: creating test automation scripts, helper code, and other programming tasks
• Case studies: real-life examples of using GenAI in testing projects
• Exercises: more advanced tasks requiring combining multiple prompt engineering techniques
• Future: open discussion on opportunities and challenges of GenAI in software testing

Teaching Methods

The workshop consists of a series of presentations and practical exercises. Through interactive collaboration, participants will gradually build a foundational set of knowledge and skills necessary for testers in a world where generative AI tools are becoming increasingly common.

Benefits

• Understanding generative AI: participants will gain a basic understanding of what generative AI is, how it works, and why it matters in testing
• Practical application: participants will learn how to work with generative AI, including prompt engineering, enabling them to incorporate tools like ChatGPT, Bard, or Midjourney into their testing practices