AI in Software Testing
This course introduces software testers to practical, real-world uses of AI in modern testing workflows. Designed to help testers reduce repetitive work and improve test coverage, the course focuses on using AI tools to generate test data, create test cases from user stories, prioritize testing based on risk, and analyze failures more effectively. Through expert-led instruction and guided labs, participants learn how to integrate AI into everyday testing activities, including CI/CD pipelines, without needing prior AI experience.
The one-day course is approximately 50% hands-on and emphasizes practical outcomes. Participants work with accessible AI tools and real testing scenarios to build skills they can immediately apply in manual, automated, or hybrid testing roles.
By the end of this course, participants will be able to:
- Use AI tools to generate realistic and varied test data
- Prioritize and select test cases based on code changes, risk, and historical results
- Generate UI, API, and functional test cases using natural-language prompts
- Identify flaky or redundant tests and improve test stability with AI-assisted techniques
- Use AI to summarize failures, detect patterns, and support faster defect triage
- Predict testing risk using commit history and past defect data
- Integrate AI-generated tests and insights into CI/CD workflows such as GitHub Actions
- Apply effective prompt-writing techniques to get accurate, useful AI outputs
- Software testers new to using AI
- QA professionals and test engineers
- SDETs and automation testers
- Manual testers looking to adopt AI-assisted workflows
- Testing professionals who want to improve efficiency, coverage, and test maintenance using AI
- Basic understanding of software testing concepts
- Experience reading and reviewing test cases
- Familiarity with functional, UI, or automated testing practices
- Basic exposure to common testing tools or frameworks
- No prior AI experience required