Course Overview
AI for Software Testing is a practical, hands-on course for experienced testers who want to integrate generative AI into real testing work—responsibly and effectively. Rather than treating AI as a testing replacement or a standalone skill, the course positions AI as a collaborator that supports testing judgment, not a substitute for it.
You will work with a small, realistic application and use AI throughout the course to explore how it can assist with understanding system behavior, generating and refining tests, structuring test ideas, exploring features, reporting defects, and making informed decisions about automation. AI output is treated as input for evaluation, not as authoritative answers.
Throughout the course, you will engage in realistic exercises that reflect how testers actually work: observing behavior, forming hypotheses, designing tests, and refining understanding as information emerges. You will use AI to generate test ideas and explanations, then examine those outputs for assumptions, gaps, executability, and risk. The emphasis is not on producing more tests faster, but on learning how to guide, question, refine, and integrate AI-generated material into sound testing work.
By the end of the course, you will have a grounded understanding of where AI adds value in testing, where it creates risk or noise, and how to maintain tester accountability while benefiting from AI’s speed and flexibility. The course is designed for testers, test analysts, and test leads who want to adopt generative AI in a professional, experience-based way—improving decision-making without compromising rigor or responsibility.
Who should attend
- Testers, Test Analysts and Developers wanting to utilize AI to automate and assess testing tasks and artefacts
- Project Managers, Business Analysts and leaders wanting to accelerate the testing process whilst balancing responsible and ethical oversight
- Anyone looking to be skilled in AI augmentation and innovation
Prerequisites
To get the most out of this course, it is recommended that participants have foundational knowledge of software testing through formal training like our Software Testing Foundations or Agile Testing course or have relevant experience working in a software testing context.
Course Objectives
- Use generative AI to explore and understand system behavior when documentation is incomplete or unclear.
- Write prompts that describe observed behavior, constraints, and test intent clearly.
- Identify assumptions, gaps, and invented details in AI-generated test cases.
- Turn AI-generated test ideas into executable tests with clear steps and observable outcomes.
- Structure tests using partitions, boundaries, state, and sequences with AI support.
- Use AI to generate exploratory testing ideas without losing tester control or focus.
- Evaluate AI-generated nonfunctional test ideas for relevance and testability.
- Write clearer bug reports and evaluate AI-assisted summaries and metrics.
- Make informed decisions about what to automate and what not to automate.
- Create a practical plan for integrating generative AI into your own testing work.
Course Content
- Understanding AI’s Role in Software Testing
- Let’s Test!
- Tests as Specifications
- Test Data
- Making Tests Executable
- Stories and Scenarios
- States and Coverage
- Validating Quality Attributes
- Test Strategy and Planning
- Bug Analysis and Reporting
- Test Automation
- Integrating AI Into Your Testing