AI-Powered Quality Engineering: From Fundamentals to Automation

Description

This eight-session hands-on course transforms manual QA testers and automation engineers into AI-powered Quality Engineers ready for project deployment. Participants progress from generative AI fundamentals through practical tool mastery — GitHub Copilot for test automation, Python with AI/ML for quality engineering, Robot Framework, and Playwright — culminating in a capstone project that demonstrates end-to-end AI-aided test automation. The course aligns to the AI+ Quality Assurance (AICerts AT-920) certification exam blueprint, preparing participants to pass the 50-question proctored exam. Each session includes instruction, guided labs, and formative assessment to validate progress.

Who This Course Is For

• QA testers and test engineers currently in manual testing roles on BFSI programs
• Associates with existing Selenium and QA automation exposure seeking AI-aided QE skills
• Test engineers transitioning from traditional automation to AI-powered testing workflows
• Associates targeting the AI+ Quality Assurance certification credential

What You Will Learn

• Explain the fundamentals of generative AI, machine learning, and deep learning — including how large language models work, their capabilities, limitations, and applications across QE workflows including performance testing.
• Apply AI-aided development practices to write, review, and improve Python test code, building sufficient Python fluency for subsequent sessions while leveraging AI assistance for development, testing, and technical writing.
• Apply structured prompt engineering techniques — role assignment, few-shot examples, chain-of-thought, and output formatting — to produce consistent QE artifacts, using GitHub Copilot as the primary demonstration vehicle and general-purpose LLMs for non-code tasks.
• Use GitHub Copilot independently to build complete test suites, critically evaluate AI suggestions, apply responsible AI practices, and integrate AI-assisted testing into CI/CD pipelines.
• Implement AI and machine learning techniques using Python to solve quality engineering problems including defect prediction, test prioritization, log analysis, and NLP-based bug triaging.
• Build and execute keyword-driven test automation suites using Robot Framework with custom Python keywords, accelerated by AI-assisted keyword generation, with exploratory and security testing techniques.
• Automate end-to-end functional and performance testing for web and mobile applications using Playwright, leveraging AI to generate selectors, page objects, and test scenarios across browsers and devices.
• Integrate AI-aided testing tools and practices into a cohesive, production-ready test automation strategy, demonstrated through a capstone project, and prepare for the AI+ Quality Assurance certification exam.

Prerequisites

• Working knowledge of software testing concepts (test cases, test plans, defect lifecycle, SDLC)
• Basic programming familiarity — Python experience helpful but not required; Selenium exposure expected
• Comfort with command-line tools and version control (Git basics)
• A GitHub account with Copilot access (organizational license to be provisioned)
• A computer with Python 3.12+, Node.js 22+, and VS Code installed
• Reliable internet connection for vILT sessions and cloud-based lab environments

Delivery Options

• Virtual instructor-led