Automation, Advanced Testing, and AI
About this course
Advance your expertise by transitioning into technical test automation and AI-augmented quality assurance. In this course, you will write automated UI test scripts using Playwright and SQL queries for robust database validation. You will gain hands-on experience in backend integration and performance testing, utilizing Apache JMeter to analyze system load and API reliability. You will also future-proof your career by leveraging Generative AI to design intelligent test cases, generate synthetic test data, and automate defect hot-spot detection. By integrating version control via Git and exploring CI/CD pipelines, you will develop the technical proficiency to introduce code-driven efficiency to your QA processes. Tools used include Playwright, Git, and VS Code (open source, preconfigured in the Linux Lab VM), GitHub (Free Tier), and Apache JMeter (free, pre-installed in the Linux VM). It's recommended to have knowledge on basic QA principles and software testing fundamentals. By the end of the course, you will validate backend states using SQL SELECT queries, automate happy-path interactions with Playwright in VS Code, execute a version control workflow from VS Code to GitHub linked to Jira, and use Generative AI to translate test logs into professional stakeholder updates.
75/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 20/35
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- 16/20
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What you'll learn
- Write automated UI test scripts using Playwright
- Validate backend states with SQL SELECT queries
- Automate interactions in VS Code with Playwright
- Execute version control workflows from VS Code to GitHub
- Utilize Generative AI for designing test cases and generating synthetic test data
Course objectives
- Gain hands-on experience in backend integration and performance testing
- Learn to leverage Generative AI for intelligent test case design
- Explore CI/CD pipelines for efficient QA processes
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