Welcome to this lesson on test case generation with AI assistance.
This lesson is going to focus on comprehensive testing strategies that form the foundation for effective AI-assisted test generation. We will examine risk-based approaches, different test levels, boundary analysis, and maintenance patterns that ensure your testing remains valuable as systems evolve.
This lesson has been designed for budding software architects and developers who are new to AI tooling generally, or those who have some experience but are looking to expand their knowledge.
This course leads onto some Instructor Led Training for building software using AI tools.
By the end of this lesson, you will have a greater understanding of:
How to prioritize testing by business risk, impact, and likelihood, and separate confidence goals from tool capabilities.
The characteristics and appropriate application of the four key test levels: Unit, Integration, Contract, and Property-based testing.
Systematic techniques for test design, including boundary analysis for numeric, temporal, concurrency, and resource boundaries.
Strategies for evaluating and validating AI-generated code, including defining acceptance criteria and using mutation testing philosophy.
This course is part of an introduction to AI tooling and we will expand your knowledge from a beginner position. However, knowledge of any AI tools would put you at an advantage.
Please note that, at the time of writing this content, all course information was accurate. AI tools evolve rapidly and as a result, minor discrepancies may appear in the course content over time. Here at QA, we strive to keep our content up to date in order to provide the best training available.
So, if you notice any information that is outdated, please report the issue. This will allow us to update the course during its next release cycle.